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17 pages, 11353 KiB  
Article
YOLO-RGDD: A Novel Method for the Online Detection of Tomato Surface Defects
by Ziheng Liang, Tingting Zhu, Guang Teng, Yajun Zhang and Zhe Gu
Foods 2025, 14(14), 2513; https://doi.org/10.3390/foods14142513 - 17 Jul 2025
Viewed by 375
Abstract
With the advancement of automation in modern agriculture, the demand for intelligence in the post-picking sorting of fruits and vegetables is increasing. As a significant global agricultural product, the defect detection and sorting of tomato is essential to ensure quality and improve economic [...] Read more.
With the advancement of automation in modern agriculture, the demand for intelligence in the post-picking sorting of fruits and vegetables is increasing. As a significant global agricultural product, the defect detection and sorting of tomato is essential to ensure quality and improve economic value. However, the traditional detection method (manual screening) is inefficient and involves high labor intensity. Therefore, a defect detection model named YOLO-RGDD is proposed based on YOLOv12s to identify five types of tomato surface defects (scars, gaps, white spots, spoilage, and dents). Firstly, the original C3k2 module and A2C2f module of YOLOv12 were replaced with RFEM in the backbone network to enhance feature extraction for small targets without increasing computational complexity. Secondly, the Dysample–Slim-Neck of the YOLO-RGDD was developed to reduce the computational complexity and enhance the detection of minor defects. Finally, dynamic convolution was used to replace the conventional convolution in the detection head in order to reduce the model parameter count. The experimental results show that the average precision, recall, and F1-score of the proposed YOLO-RGDD model for tomato defect detection reach 88.5%, 85.7%, and 87.0%, respectively, surpassing advanced object recognition detection algorithms. Additionally, the computational complexity of the YOLO-RGDD is 16.1 GFLOPs, which is 24.8% lower than that of the original YOLOv12s model (21.4 GFLOPs), facilitating the model’s deployment in automated agricultural production. Full article
(This article belongs to the Section Food Engineering and Technology)
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20 pages, 2968 KiB  
Article
Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
by Xiaofei Ma, Nannan Shen, Yanhui He, Zhuo Fang, Hongyan Zhang, Yun Wang and Jinrong Duan
Appl. Sci. 2025, 15(13), 7468; https://doi.org/10.3390/app15137468 - 3 Jul 2025
Viewed by 258
Abstract
During the cultivation and circulation of Chinese mitten crab (Eriocheir sinensis), the difficulty in tracing geographic origin leads to quality uncertainty and market disorder. To address this challenge, this study proposes a two-stage origin traceability framework that integrates a lightweight object detector and [...] Read more.
During the cultivation and circulation of Chinese mitten crab (Eriocheir sinensis), the difficulty in tracing geographic origin leads to quality uncertainty and market disorder. To address this challenge, this study proposes a two-stage origin traceability framework that integrates a lightweight object detector and a high-precision classifier. In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. In the second stage, an Improved GoogleNet’s Inception Net for Crab is developed based on the Inception module, with further integration of Asymmetric Convolution Blocks and Squeeze and Excitation modules to improve the feature extraction and classification ability for regional origin. A comprehensive crab dataset is constructed, containing images from diverse farming sites, including variations in species, color, size, angle, and background conditions. Experimental results show that the proposed detector achieves an mAP50 of 99.5% and an mAP50-95 of 88.5%, while maintaining 309 FPS and reducing GFLOPs by 35.3%. Meanwhile, the classification model achieves high accuracy with only 17.4% and 40% of the parameters of VGG16 and AlexNet, respectively. In conclusion, the proposed method achieves an optimal accuracy-speed-complexity trade-off, enabling robust real-time traceability for aquaculture systems. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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20 pages, 4488 KiB  
Article
OMB-YOLO-tiny: A Lightweight Detection Model for Damaged Pleurotus ostreatus Based on Enhanced YOLOv8n
by Lei Shi, Zhuo Bai, Xiangmeng Yin, Zhanchen Wei, Haohai You, Shilin Liu, Fude Wang, Xuexi Qi, Helong Yu, Chunguang Bi and Ruiqing Ji
Horticulturae 2025, 11(7), 744; https://doi.org/10.3390/horticulturae11070744 - 27 Jun 2025
Viewed by 317
Abstract
Pleurotus ostreatus, classified under the phylum Basidiomycota, order Agaricales, and family Pleurotaceae, is a prevalent gray edible fungus. Its physical damage not only compromises quality and appearance but also significantly diminishes market value. This study proposed an enhanced method for detecting Pleurotus [...] Read more.
Pleurotus ostreatus, classified under the phylum Basidiomycota, order Agaricales, and family Pleurotaceae, is a prevalent gray edible fungus. Its physical damage not only compromises quality and appearance but also significantly diminishes market value. This study proposed an enhanced method for detecting Pleurotus ostreatus damage based on an improved YOLOv8n model, aiming to advance the accessibility of damage recognition technology, enhance automation in Pleurotus cultivation, and reduce labor dependency. This approach holds critical implications for agricultural modernization and serves as a pivotal step in advancing China’s agricultural modernization, while providing valuable references for subsequent research. Utilizing a self-collected, self-organized, and self-constructed dataset, we modified the feature extraction module of the original YOLOv8n by integrating a lightweight GhostHGNetv2 backbone network. During the feature fusion stage, the original YOLOv8 components were replaced with a lightweight SlimNeck network, and an Attentional Scale Sequence Fusion (ASF) mechanism was incorporated into the feature fusion architecture, resulting in the proposed OMB-YOLO model. This model achieves a remarkable balance between parameter efficiency and detection accuracy, attaining a parameter of 2.24 M and a mAP@0.5 of 90.11% on the test set. To further optimize model lightweighting, the DepGraph method was applied for pruning the OMB-YOLO model, yielding the OMB-YOLO-tiny variant. Experimental evaluations on the damaged Pleurotus dataset demonstrate that the OMB-YOLO-tiny model outperforms mainstream models in both accuracy and inference speed while reducing parameters by nearly half. With a parameter of 1.72 M and mAP@0.5 of 90.14%, the OMB-YOLO-tiny model emerges as an optimal solution for detecting Pleurotus ostreatus damage. These results validate its efficacy and practical applicability in agricultural quality control systems. Full article
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24 pages, 9307 KiB  
Article
DASS-YOLO: Improved YOLOv7-Tiny with Attention-Guided Shape Awareness and DySnakeConv for Spray Code Defect Detection
by Yixuan Shi, Shiling Zheng, Meiyue Bian, Xia Zhang and Lishan Yang
Symmetry 2025, 17(6), 906; https://doi.org/10.3390/sym17060906 - 8 Jun 2025
Viewed by 531
Abstract
To address the challenges of detecting spray code defects caused by complex morphological variations and the discrete characterization of dot-matrix spray codes, an improved YOLOv7-tiny algorithm named DASS-YOLO is proposed. Firstly, the DySnakeConv module is employed in Backbone–Neck cross-layer connections. With a dynamic [...] Read more.
To address the challenges of detecting spray code defects caused by complex morphological variations and the discrete characterization of dot-matrix spray codes, an improved YOLOv7-tiny algorithm named DASS-YOLO is proposed. Firstly, the DySnakeConv module is employed in Backbone–Neck cross-layer connections. With a dynamic structure and adaptive learning, it can capture the complex morphological features of spray codes. Secondly, we proposed an Attention-guided Shape Enhancement Module with CAA (ASEM-CAA), which adopts a symmetrical dual-branch structure to facilitate bidirectional interaction between local and global features, enabling precise prediction of the overall spray code shape. It also reduces feature discontinuity in dot-matrix codes, ensuring a more coherent representation. Furthermore, Slim-neck, which is famous for its more lightweight structure, is adopted in the Neck to reduce model complexity while maintaining accuracy. Finally, Shape-IoU is applied to improve the accuracy of the bounding box regression. Experiments show that DASS-YOLO improves the detection accuracy by 1.9%. Additionally, for small defects such as incomplete code and code spot, the method achieves better accuracy improvements of 8.7% and 2.1%, respectively. Full article
(This article belongs to the Special Issue Symmetry and Its Applications in Computer Vision)
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22 pages, 5073 KiB  
Article
Deep Learning-Assisted Microscopic Polarization Inspection of Micro-Nano Damage Precursors: Automatic, Non-Destructive Metrology for Additive Manufacturing Devices
by Dingkang Li, Xing Peng, Zhenfeng Ye, Hongbing Cao, Bo Wang, Xinjie Zhao and Feng Shi
Nanomaterials 2025, 15(11), 821; https://doi.org/10.3390/nano15110821 - 29 May 2025
Viewed by 399
Abstract
Additive Manufacturing (AM), as a revolutionary breakthrough in advanced manufacturing paradigms, leverages its unique layer-by-layer construction advantage to exhibit significant technological superiority in the fabrication of complex structural components for aerospace, biomedical, and other fields. However, when addressing industrial-grade precision manufacturing requirements, key [...] Read more.
Additive Manufacturing (AM), as a revolutionary breakthrough in advanced manufacturing paradigms, leverages its unique layer-by-layer construction advantage to exhibit significant technological superiority in the fabrication of complex structural components for aerospace, biomedical, and other fields. However, when addressing industrial-grade precision manufacturing requirements, key challenges such as the multi-scale characteristics of surface damage precursors, interference from background noise, and the scarcity of high-quality training samples severely constrain the intelligent transformation of AM quality monitoring systems. This study proposes an innovative microscopic polarization YOLOv11-LSF intelligent inspection framework, which establishes an automated non-destructive testing methodology for AM device micro-nano damage precursors through triple technological innovations, effectively breaking through existing technical bottlenecks. Firstly, a multi-scale perception module is constructed based on the Large Separable Kernel Attention mechanism, significantly enhancing the network’s feature detection capability in complex industrial scenarios. Secondly, the cross-level local network VoV-GSCSP module is designed utilizing GSConv and a one-time aggregation method, resulting in a Slim-neck architecture that significantly reduces model complexity without compromising accuracy. Thirdly, an innovative simulation strategy incorporating physical features for damage precursors is proposed, constructing a virtual and real integrated training sample library and breaking away from traditional deep learning reliance on large-scale labeled data. Experimental results demonstrate that compared to the baseline model, the accuracy (P) of the YOLOv11-LSF model is increased by 1.6%, recall (R) by 1.6%, mAP50 by 1.5%, and mAP50-95 by 2.8%. The model hits an impressive detection accuracy of 99% for porosity-related micro-nano damage precursors and remains at 94% for cracks. Its unique small sample adaptation capability and robustness under complex conditions provide a reliable technical solution for industrial-grade AM quality monitoring. This research advances smart manufacturing quality innovation and enables cross-scale micro-nano damage inspection in advanced manufacturing. Full article
(This article belongs to the Section Nanofabrication and Nanomanufacturing)
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21 pages, 29272 KiB  
Article
Multi-Strategy Enhancement of YOLOv8n Monitoring Method for Personnel and Vehicles in Mine Air Door Scenarios
by Lei Zhang, Hongjing Tao, Zhipeng Sun and Weixun Yi
Sensors 2025, 25(10), 3128; https://doi.org/10.3390/s25103128 - 15 May 2025
Viewed by 499
Abstract
The mine air door is the primary facility for regulating airflow and controlling the passage of personnel and vehicles. Intelligent monitoring of personnel and vehicles within the mine air door system is a crucial measure to ensure the safety of mine operations. To [...] Read more.
The mine air door is the primary facility for regulating airflow and controlling the passage of personnel and vehicles. Intelligent monitoring of personnel and vehicles within the mine air door system is a crucial measure to ensure the safety of mine operations. To address the issues of slow speed and low efficiency associated with traditional detection methods in mine air door scenarios, this study proposes a CGSW-YOLO man-vehicle monitoring model based on YOLOv8n. Firstly, the Faster Block module, which incorporates partial convolution (PConv), is integrated with the C2f module of the backbone network. This combination aims to minimize redundant calculations during the convolution process and expedite the model’s aggregation of multi-scale information. Secondly, standard convolution is replaced with GhostConv in the backbone network to further reduce the number of model parameters. Additionally, the Slim-neck module is integrated into the neck feature fusion network to enhance the information fusion capability of various feature maps while maintaining detection accuracy. Finally, WIoUv3 is utilized as the loss function, and a dynamic non-monotonic focusing mechanism is implemented to adjust the quality of the anchor frame dynamically. The experimental results indicate that the CGSW-YOLO model exhibits strong performance in monitoring man-vehicle interactions in mine air door scenarios. The Precision (P), Recall (R), and the map@0.5 are recorded at 88.2%, 93.9%, and 98.0%, respectively, representing improvements of 0.2%, 1.5%, and 1.7% over the original model. The Frames Per Second (FPS) has increased to 135.14 f·s−1, reflecting a rise of 35.14%. Additionally, the parameters, the floating point operations per second (FLOPS), and model size are 2.36 M, 6.2 G, and 5.0 MB, respectively. These values indicate reductions of 21.6%, 23.5%, and 20.6% compared to the original model. Through the verification of on-site surveillance video, the CGSW-YOLO model demonstrates its effectiveness in monitoring both individuals and vehicles in scenarios involving mine air doors. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
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24 pages, 5775 KiB  
Article
GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm
by Xiangqiang Kong, Guangmin Liu and Yanchen Gao
Sensors 2025, 25(10), 3052; https://doi.org/10.3390/s25103052 - 12 May 2025
Viewed by 687
Abstract
Printed circuit boards (PCBs) are an indispensable part of electronic products, and their quality is crucial to the operational integrity and functional reliability of these products. Currently, existing PCB defect detection models are beset with issues such as excessive model size and parameter [...] Read more.
Printed circuit boards (PCBs) are an indispensable part of electronic products, and their quality is crucial to the operational integrity and functional reliability of these products. Currently, existing PCB defect detection models are beset with issues such as excessive model size and parameter complexity, rendering them ill-equipped to meet the requirements for lightweight deployment on mobile devices. To address this challenge, this paper proposes a lightweight detection model, GESC-YOLO, developed through modifications to the YOLOv8n architecture. First, a new lightweight module, C2f-GE, is designed to replace the C2f module of the backbone network, which effectively reduces the computational parameters, and at the same time increases the number of channels of the feature map to enhance the feature extraction capability of the model. Second, the neck network employs the lightweight hybrid convolution GSConv. By integrating it with the VoV-GSCSP module, the Slim-neck structure is constructed. This approach not only guarantees detection precision but also enables model lightweighting and a reduction in the number of parameters. Finally, the coordinate attention is introduced into the neck network to decompose the channel attention and aggregate the features, which can effectively retain the spatial information and thus improve the detection and localization accuracy of tiny defects (defect area less than 1% of total image area) in PCB defect images. Experimental results demonstrate that, in contrast to the original YOLOv8n model, the GESC-YOLO algorithm boosts the mean Average Precision (mAP) of PCB surface defects by 0.4%, reaching 99%. Simultaneously, the model size is reduced by 25.4%, the parameter count is cut down by 28.6%, and the computational resource consumption is reduced by 26.8%. This successfully achieves the harmonization of detection precision and model lightweighting. Full article
(This article belongs to the Section Sensing and Imaging)
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24 pages, 13830 KiB  
Article
FLE-YOLO: A Faster, Lighter, and More Efficient Strategy for Autonomous Tower Crane Hook Detection
by Xin Hu, Xiyu Wang, Yashu Chang, Jian Xiao, Hongliang Cheng and Firdaousse Abdelhad
Appl. Sci. 2025, 15(10), 5364; https://doi.org/10.3390/app15105364 - 11 May 2025
Viewed by 595
Abstract
To address the complexities of crane hook operating environments, the challenges faced by large-scale object detection algorithms on edge devices, and issues such as frame rate mismatch causing image delays, this paper proposes a faster, lighter, and more efficient object detection algorithm called [...] Read more.
To address the complexities of crane hook operating environments, the challenges faced by large-scale object detection algorithms on edge devices, and issues such as frame rate mismatch causing image delays, this paper proposes a faster, lighter, and more efficient object detection algorithm called FLE-YOLO. Firstly, the FasterNet is used as the backbone for feature extraction, and the Triplet Attention mechanism is integrated to effectively emphasize target information while maintaining network lightweightness effectively. Additionally, the Slim-neck module is introduced in the neck connection layer, utilizing a lightweight convolutional network GSconv to further streamline the network structure without compromising recognition accuracy. Lastly, the Dyhead module is employed in the head section to unify multiple attention operations, improve the ability to resist interference from small objects and complex backgrounds. Experimental evaluations on public datasets VOC2012 and COCO2017 demonstrate the effectiveness of our proposed algorithm in terms of lightweight design and detection accuracy. Experimental evaluations were also conducted using images of crane hooks captured under complex operating conditions. The results demonstrate that compared to the original algorithm, the proposed approach achieves a reduction in computational complexity to 19.4 GFLOPs, an increase in FPS to 142.857 f/s, and the precision reached 97.3%. Additionally, the AP50 reaches 98.3%, reflecting 0.6% improvement. Ultimately, the testing carried out at the construction site successfully facilitated the identification and tracking of hooks, thereby ensuring the safety and efficiency of tower crane operations. Full article
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22 pages, 8831 KiB  
Article
YOLOv8n-SMMP: A Lightweight YOLO Forest Fire Detection Model
by Nianzu Zhou, Demin Gao and Zhengli Zhu
Fire 2025, 8(5), 183; https://doi.org/10.3390/fire8050183 - 3 May 2025
Cited by 4 | Viewed by 1284
Abstract
Global warming has driven a marked increase in forest fire occurrences, underscoring the critical need for timely and accurate detection to mitigate fire-related losses. Existing forest fire detection algorithms face limitations in capturing flame and smoke features in complex natural environments, coupled with [...] Read more.
Global warming has driven a marked increase in forest fire occurrences, underscoring the critical need for timely and accurate detection to mitigate fire-related losses. Existing forest fire detection algorithms face limitations in capturing flame and smoke features in complex natural environments, coupled with high computational complexity and inadequate lightweight design for practical deployment. To address these challenges, this paper proposes an enhanced forest fire detection model, YOLOv8n-SMMP (SlimNeck–MCA–MPDIoU–Pruned), based on the YOLO framework. Key innovations include the following: introducing the SlimNeck solution to streamline the neck network by replacing conventional convolutions with Group Shuffling Convolution (GSConv) and substituting the Cross-convolution with 2 filters (C2f) module with the lightweight VoV-based Group Shuffling Cross-Stage Partial Network (VoV-GSCSP) feature extraction module; integrating the Multi-dimensional Collaborative Attention (MCA) mechanism between the neck and head networks to enhance focus on fire-related regions; adopting the Minimum Point Distance Intersection over Union (MPDIoU) loss function to optimize bounding box regression during training; and implementing selective channel pruning tailored to the modified network architecture. The experimental results reveal that, relative to the baseline model, the optimized lightweight model achieves a 3.3% enhancement in detection accuracy (mAP@0.5), slashes the parameter count by 31%, and reduces computational overhead by 33%. These advancements underscore the model’s superior performance in real-time forest fire detection, outperforming other mainstream lightweight YOLO models in both accuracy and efficiency. Full article
(This article belongs to the Special Issue Intelligent Forest Fire Prediction and Detection)
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34 pages, 16178 KiB  
Article
YOLO11-PGM: High-Precision Lightweight Pomegranate Growth Monitoring Model for Smart Agriculture
by Rijun Wang, Yesheng Chen, Guanghao Zhang, Chunhui Yang, Xianglong Teng and Changjun Zhao
Agronomy 2025, 15(5), 1123; https://doi.org/10.3390/agronomy15051123 - 1 May 2025
Cited by 1 | Viewed by 1083
Abstract
As a vital cash crop, intelligent monitoring of pomegranate growth stages plays a crucial role in improving orchard management efficiency and yield. Current research on pomegranates primarily focuses on the detection and quality classification of diseases and pests. Furthermore, the difficulty in deploying [...] Read more.
As a vital cash crop, intelligent monitoring of pomegranate growth stages plays a crucial role in improving orchard management efficiency and yield. Current research on pomegranates primarily focuses on the detection and quality classification of diseases and pests. Furthermore, the difficulty in deploying complex models in practical scenarios hinders the widespread adoption of pomegranate monitoring technology. To address these challenges, this paper proposes a lightweight pomegranate growth stage detection model, YOLO11-PGM, based on YOLO11n. The model integrates several innovative designs, including a Multi-Scale Edge Enhancement (MSEE) module to mitigate the effects of leaf and fruit occlusion, a Slim Shared Convolutional Head (SSCH) to resolve feature inconsistency across different scales in the feature pyramid, and a High-level Screening Feature Pyramid Network (HSFPN) to replace the standard neck network and achieve a balance between accuracy and complexity. Experimental results demonstrate that YOLO11-PGM achieves an accuracy of 92.3%, a recall of 86.3%, and an mAP50 of 94.0% with only 1.63 M parameters, 4.8 G FLOPs, and a model size of 3.7 MB. It outperforms YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and YOLOv10s. Compared with the baseline YOLO11n, YOLO11-PGM improves the mAP50 by 2.6%, reduces the number of parameters by 36.9%, decreases computational complexity by 23.8%, and shrinks the model size by 32.7%. This model offers an effective solution for intelligent monitoring of pomegranate growth stages and provides valuable theoretical and technical references for orchard yield prediction, growth monitoring, planting management optimization, and the development of automated harvesting systems. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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29 pages, 8507 KiB  
Article
ASE-YOLOv8n: A Method for Cherry Tomato Ripening Detection
by Xuemei Liang, Haojie Jia, Hao Wang, Lijuan Zhang, Dongming Li, Zhanchen Wei, Haohai You, Xiaoru Wan, Ruixin Li, Wei Li and Minglai Yang
Agronomy 2025, 15(5), 1088; https://doi.org/10.3390/agronomy15051088 - 29 Apr 2025
Cited by 3 | Viewed by 944
Abstract
To enhance the efficiency of automatic cherry tomato harvesting in precision agriculture, an improved YOLOv8n algorithm was proposed for fast and accurate recognition in natural environments. The improvements are as follows: first, the ADown down-sampling module replaces part of the original network backbone’s [...] Read more.
To enhance the efficiency of automatic cherry tomato harvesting in precision agriculture, an improved YOLOv8n algorithm was proposed for fast and accurate recognition in natural environments. The improvements are as follows: first, the ADown down-sampling module replaces part of the original network backbone’s standard convolution, enabling the model to capture higher-level image features for more accurate target detection, while also reducing model complexity by cutting the number of parameters. Secondly, the model’s neck adopts a Slim-Neck (GSConv+VoV-GSCSP) instead of traditional convolution with C2f. It replaces this combination with the more efficient CSConv and swaps the C2f module for VoV-GSCSP. Finally, the model also introduces the EMA attention mechanism, implemented at the P5 layer, which enhances the feature representation capability, enabling the network to extract detailed target features more accurately. This study trained the object-detection algorithm on a self-built cherry tomato dataset before and after improvement and compared it with early deep learning models and YOLO series algorithms. The experimental results show that the improved model increases accuracy by 3.18%, recall by 1.43%, the F1 score by 2.30%, mAP50 by 1.57%, and mAP50-95 by 1.37%. Additionally, the number of parameters is reduced to 2.52 M, and the model size is reduced to 5.08 MB, which outperforms other related models compared to the previous version. The experiment demonstrates the technology’s broad potential for embedded systems and mobile devices. The improved model offers efficient, accurate support for automated cherry tomato harvesting. Full article
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15 pages, 4840 KiB  
Article
Research on Method for Intelligent Recognition of Deep-Sea Biological Images Based on PSVG-YOLOv8n
by Dali Chen, Xianpeng Shi, Jichao Yang, Xiang Gao and Yugang Ren
J. Mar. Sci. Eng. 2025, 13(4), 810; https://doi.org/10.3390/jmse13040810 - 18 Apr 2025
Viewed by 426
Abstract
Deep-sea biological detection is a pivotal technology for the exploration and conservation of marine resources. Nonetheless, the inherent complexities of the deep-sea environment, the scarcity of available deep-sea organism samples, and the significant refraction and scattering effects of underwater light collectively impose formidable [...] Read more.
Deep-sea biological detection is a pivotal technology for the exploration and conservation of marine resources. Nonetheless, the inherent complexities of the deep-sea environment, the scarcity of available deep-sea organism samples, and the significant refraction and scattering effects of underwater light collectively impose formidable challenges on the current detection algorithms. To address these issues, we propose an advanced deep-sea biometric identification framework based on an enhanced YOLOv8n architecture, termed PSVG-YOLOv8n. Specifically, our model integrates a highly efficient Partial Spatial Attention module immediately preceding the SPPF layer in the backbone, thereby facilitating the refined, localized feature extraction of deep-sea organisms. In the neck network, a Slim-Neck module (GSconv + VoVGSCSP) is incorporated to reduce the parameter count and model size while simultaneously augmenting the detection performance. Moreover, the introduction of a squeeze–excitation residual module (C2f_SENetV2), which leverages a multi-branch fully connected layer, further bolsters the network’s global representational capacity. Finally, an improved detection head synergistically fuses all the modules, yielding substantial enhancements in the overall accuracy. Experiments conducted on a dataset of deep-sea images acquired by the Jiaolong manned submersible indicate that the proposed PSVG-YOLOv8n model achieved a precision of 79.9%, an mAP50 of 67.2%, and an mAP50-95 of 50.9%. These performance metrics represent improvements of 1.2%, 2.3%, and 1.1%, respectively, over the baseline YOLOv8n model. The observed enhancements underscore the effectiveness of the proposed modifications in addressing the challenges associated with deep-sea organism detection, thereby providing a robust framework for accurate deep-sea biological identification. Full article
(This article belongs to the Section Ocean Engineering)
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19 pages, 8023 KiB  
Article
Slim-YOLO: An Improved Sugarcane Tail Tip Recognition Algorithm Based on YOLO11n for Complex Field Environments
by Chunming Wen, Yang Cheng, Shangping Li, Leilei Liu, Qingquan Liang, Kaihua Li and Youzong Huang
Appl. Sci. 2025, 15(8), 4286; https://doi.org/10.3390/app15084286 - 13 Apr 2025
Viewed by 469
Abstract
Accurate identification of the sugarcane tail tip is crucial for the real-time automation control of the harvester’s cutting device, improving harvesting efficiency, and reducing impurity rates. This paper proposes Slim-YOLO, an improved YOLO11n-based algorithm incorporating a lightweight RepViT backbone, an ELANSlimNeck neck structure, [...] Read more.
Accurate identification of the sugarcane tail tip is crucial for the real-time automation control of the harvester’s cutting device, improving harvesting efficiency, and reducing impurity rates. This paper proposes Slim-YOLO, an improved YOLO11n-based algorithm incorporating a lightweight RepViT backbone, an ELANSlimNeck neck structure, and the Unified-IoU (UIoU) loss function. Experimental results on the sugarcane tailing dataset show that Slim-YOLO achieves an mAP50 of 92.2% and mAP50:95 of 48.2%, outperforming YOLO11n by 8.2% and 6.1%, respectively, while reducing parameters by 48.4%. The enhanced accuracy and lightweight design make it suitable for practical deployment, offering theoretical and technical support for the automation control of sugarcane harvesters. Full article
(This article belongs to the Section Agricultural Science and Technology)
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21 pages, 8405 KiB  
Article
YOLOv11-BSS: Damaged Region Recognition Based on Spatial and Channel Synergistic Attention and Bi-Deformable Convolution in Sanding Scenarios
by Yinjiang Li, Zhifeng Zhou and Ying Pan
Electronics 2025, 14(7), 1469; https://doi.org/10.3390/electronics14071469 - 5 Apr 2025
Cited by 1 | Viewed by 845
Abstract
In order to address the problem that the paint surface of the damaged region of the body is similar to the color texture characteristics of the usual paint surface, which leads to the phenomenon of leakage or misdetection in the detection process, an [...] Read more.
In order to address the problem that the paint surface of the damaged region of the body is similar to the color texture characteristics of the usual paint surface, which leads to the phenomenon of leakage or misdetection in the detection process, an algorithm for detecting the damaged region of the body based on the improved YOLOv11 is proposed. Firstly, bi-deformable convolution is proposed to optimize the convolution kernel shape offset direction, which effectively improves the feature representation power of the backbone network; secondly, the C2PSA-SCSA module is designed to construct the coupling between spatial attention and channel attention, which enhances the perceptual power of the backbone network, and makes the model pay better attention to the damaged region features. Then, based on the GSConv module and the DWConv module, we build the slim-neck feature fusion network based on the GSConv module and DWConv module, which effectively fuses local features and global features to improve the saturation of semantic features; finally, the Focaler-CIoU border loss function is designed, which makes use of the principle of Focaler-IoU segmented linear mapping, adjusts the border loss function’s attention to different samples, and improves the model’s convergence of feature learning at various scales. The experimental results show that the enhanced YOLOv11-BSS network improves the precision rate by 7.9%, the recall rate by 1.4%, and the mAP@50 by 3.7% over the baseline network, which effectively reduces the leakage and misdetection of the damaged areas of the car body. Full article
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19 pages, 8449 KiB  
Article
Object Detection Method of Inland Vessel Based on Improved YOLO
by Yaoqi Wang, Jiasheng Song, Yichun Wang, Rongjie Wang and Hongyu Chen
J. Mar. Sci. Eng. 2025, 13(4), 697; https://doi.org/10.3390/jmse13040697 - 31 Mar 2025
Viewed by 575
Abstract
In order to solve the problems of low accuracy of the current mainstream target detection algorithms in identifying small target ships, complex background interference such as coastline buildings and trees, and the influence of ship occlusion on ship target detection, an inland river [...] Read more.
In order to solve the problems of low accuracy of the current mainstream target detection algorithms in identifying small target ships, complex background interference such as coastline buildings and trees, and the influence of ship occlusion on ship target detection, an inland river ship detection method based on improved YOLOv10n: CDS-YOLO is proposed under the premise of keeping the model lightweight. Firstly, the CAA attention module is introduced into the Backbone network, and the C2f_CAA module is constructed at the same time to enhance the features of the central region and improve the understanding ability of complex scenes. Then, the Conv of the Backbone network was replaced with DBB to enhance the expression ability of a single convolution and enrich the feature space. Finally, GSConv and VovGSCSP in Slim-Neck are introduced into the Neck network to optimize the network architecture, reduce part of the model complexity, and further improve the performance of the model. Experimental results show that compared with YOLOv10n, CDS-YOLO has the detection accuracy, recall rate and mAP@0.5 increased by 3.7%, 2% and 0.9% respectively, reaching 98.4%, 97.4% and 99.2% respectively, indicating that CDS-YOLO has good accuracy and robustness in the detection and classification of inshore ships. Full article
(This article belongs to the Section Coastal Engineering)
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