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Keywords = Biformer attention mechanism

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19 pages, 3527 KiB  
Article
BBW YOLO: Intelligent Detection Algorithms for Aluminium Profile Material Surface Defects
by Zijuan Yin, Haichao Li, Bo Qi and Guangyue Shan
Coatings 2025, 15(6), 684; https://doi.org/10.3390/coatings15060684 - 6 Jun 2025
Cited by 1 | Viewed by 544
Abstract
This study aims to address the issue of various defects on the surface of aluminum profile materials, which can significantly impact industrial production as well as the reliability and safety of products. An algorithmic model, BBW YOLO (YOLOv8-BiFPN-BiFormer-WIoU v3), based on an enhanced [...] Read more.
This study aims to address the issue of various defects on the surface of aluminum profile materials, which can significantly impact industrial production as well as the reliability and safety of products. An algorithmic model, BBW YOLO (YOLOv8-BiFPN-BiFormer-WIoU v3), based on an enhanced YOLOv8 model is proposed for aluminum profile material surface-defect detection. First, the model can effectively eliminate redundant feature information and enhance the feature-extraction process by incorporating a weighted Bidirectional Feature Pyramid Feature-fusion Network (BiFPN). Second, the model incorporates a dynamic sparse-attention mechanism (BiFormer) along with an efficient pyramidal network architecture, which enhances the precision and detection speed of the model. Meanwhile, the model optimizes the loss function using Wise-IoU v3 (WIoU v3), which effectively enhances the localization performance of surface-defect detection. The experimental results demonstrate that the precision and recall of the BBW YOLO model are improved by 5% and 2.65%, respectively, compared with the original YOLOv8 model. Notably, the BBW YOLO model achieved a real-time detection speed of 292.3 f/s. In addition, the model size of BBW YOLO is only 6.3 MB. At the same time, the floating-point operations of BBW YOLO are reduced to 8.3 G. As a result, the BBW YOLO model offers excellent defect detection performance and opens up new opportunities for its efficient development in the aluminum industry. Full article
(This article belongs to the Special Issue Solid Surfaces, Defects and Detection, 2nd Edition)
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22 pages, 34022 KiB  
Article
A Lightweight Citrus Object Detection Method in Complex Environments
by Qiurong Lv, Fuchun Sun, Yuechao Bian, Haorong Wu, Xiaoxiao Li, Xin Li and Jie Zhou
Agriculture 2025, 15(10), 1046; https://doi.org/10.3390/agriculture15101046 - 12 May 2025
Viewed by 549
Abstract
Aiming at the limitations of current citrus detection methods in complex orchard environments, especially the problems of poor model adaptability and high computational complexity under different lighting, multiple occlusions, and dense fruit conditions, this study proposes an improved citrus detection model, YOLO-PBGM, based [...] Read more.
Aiming at the limitations of current citrus detection methods in complex orchard environments, especially the problems of poor model adaptability and high computational complexity under different lighting, multiple occlusions, and dense fruit conditions, this study proposes an improved citrus detection model, YOLO-PBGM, based on You Only Look Once v7 (YOLOv7). First, to tackle the large size of the YOLOv7 network model and its deployment challenges, the PC-ELAN module is constructed by introducing Partial Convolution (PConv) for lightweight improvement, which reduces the model’s demand for computing resources and parameters. At the same time, the Bi-Former attention module is embedded to enhance the perception and processing of citrus fruit information. Secondly, a lightweight neck network is constructed using Grouped Shuffle Convolution (GSConv) to simplify computational complexity. Finally, the minimum-point-distance-based IoU (MPDIoU) loss function is utilized to optimize the boundary return mechanism, which speeds up model convergence and reduces the redundancy of bounding box regression. Experimental results indicate that for the citrus dataset collected in a natural environment, the improved model reduces Params and GFLOPs by 15.4% and 23.7%, respectively, while improving precision, recall, and mAP by 0.3%, 4%, and 3.5%, respectively, thereby outperforming other detection networks. Additionally, an analysis of citrus object detection under varying lighting and occlusion conditions reveals that the YOLO-PBGM network model demonstrates good adaptability, effectively coping with variations in lighting and occlusions while exhibiting high robustness. This model can provide a technical reference for uncrewed intelligent picking of citrus. Full article
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13 pages, 4428 KiB  
Article
YOLO-CBF: Optimized YOLOv7 Algorithm for Helmet Detection in Road Environments
by Zhiqiang Wu, Jiaohua Qin, Xuyu Xiang and Yun Tan
Electronics 2025, 14(7), 1413; https://doi.org/10.3390/electronics14071413 - 31 Mar 2025
Viewed by 515
Abstract
Helmet-wearing detection for electric vehicle riders is essential for traffic safety, yet existing detection models often suffer from high target occlusion and low detection accuracy in complex road environments. To address these issues, this paper proposes YOLO-CBF, an improved YOLOv7-based detection network. The [...] Read more.
Helmet-wearing detection for electric vehicle riders is essential for traffic safety, yet existing detection models often suffer from high target occlusion and low detection accuracy in complex road environments. To address these issues, this paper proposes YOLO-CBF, an improved YOLOv7-based detection network. The proposed model integrates coordinate convolution to enhance spatial information perception, optimizes the Focal EIOU loss function, and incorporates the BiFormer dynamic sparse attention mechanism to achieve more efficient computation and dynamic content perception. These enhancements enable the model to extract key features more effectively, improving detection precision. Experimental results show that YOLO-CBF achieves an average mAP of 95.6% for helmet-wearing detection in various scenarios, outperforming the original YOLOv7 by 4%. Additionally, YOLO-CBF demonstrates superior performance compared to other mainstream object detection models, achieving accurate and reliable helmet detection for electric vehicle riders. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications)
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16 pages, 2980 KiB  
Article
RF-YOLOv7: A Model for the Detection of Poor-Quality Grapes in Natural Environments
by Changyong Li, Shunchun Zhang and Zhijie Ma
Agriculture 2025, 15(4), 387; https://doi.org/10.3390/agriculture15040387 - 12 Feb 2025
Viewed by 786
Abstract
This study addresses the challenges of detecting inferior fruits in table grapes in natural environments, focusing on subtle appearance differences, occlusions, and fruit overlaps. We propose an enhanced green grape fruit disease detection model named RF-YOLOv7. The model is trained on a dataset [...] Read more.
This study addresses the challenges of detecting inferior fruits in table grapes in natural environments, focusing on subtle appearance differences, occlusions, and fruit overlaps. We propose an enhanced green grape fruit disease detection model named RF-YOLOv7. The model is trained on a dataset comprising images of small fruits, sunburn, excess grapes, fruit fractures, and poor-quality grape bunches. RF-YOLOv7 builds upon the YOLOv7 architecture by integrating four Contextual Transformer (CoT) modules to improve target-detection accuracy, employing the Wise-IoU (WIoU) loss function to enhance generalization and overall performance, and introducing the Bi-Former attention mechanism for dynamic query awareness sparsity. The experimental results demonstrate that RF-YOLOv7 achieves a detection accuracy of 83.5%, recall rate of 76.4%, mean average precision (mAP) of 80.1%, and detection speed of 58.8 ms. Compared to the original YOLOv7, RF-YOLOv7 exhibits a 3.5% increase in mAP, with only an 8.3 ms increase in detection time. This study lays a solid foundation for the development of automatic detection equipment for intelligent grape pruning. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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17 pages, 8493 KiB  
Article
Copper Nodule Defect Detection in Industrial Processes Using Deep Learning
by Zhicong Zhang, Xiaodong Huang, Dandan Wei, Qiqi Chang, Jinping Liu and Qingxiu Jing
Information 2024, 15(12), 802; https://doi.org/10.3390/info15120802 - 11 Dec 2024
Cited by 1 | Viewed by 1282
Abstract
Copper electrolysis is a crucial process in copper smelting. The surface of cathodic copper plates is often affected by various electrolytic process factors, resulting in the formation of nodule defects that significantly impact surface quality and disrupt the downstream production process, making the [...] Read more.
Copper electrolysis is a crucial process in copper smelting. The surface of cathodic copper plates is often affected by various electrolytic process factors, resulting in the formation of nodule defects that significantly impact surface quality and disrupt the downstream production process, making the prompt detection of these defects essential. At present, the detection of cathode copper plate nodules is performed by manual identification. In order to address the issues with manual convex nodule identification on the surface of industrial cathode copper plates in terms of low accuracy, high effort, and low efficiency in the manufacturing process, a lightweight YOLOv5 model combined with the BiFormer attention mechanism is proposed in this paper. The model employs MobileNetV3, a lightweight feature extraction network, as its backbone, reducing the parameter count and computational complexity. Additionally, an attention mechanism is introduced to capture multi-scale information, thereby enhancing the accuracy of nodule recognition. Meanwhile, the F-EIOU loss function is employed to strengthen the model’s robustness and generalization ability, effectively addressing noise and imbalance issues in the data. Experimental results demonstrate that the improved YOLOv5 model achieves a precision of 92.71%, a recall of 91.24%, and a mean average precision (mAP) of 92.69%. Moreover, a single-frame detection time of 4.61 ms is achieved by the model, which has a size of 2.91 MB. These metrics meet the requirements of practical production and provide valuable insights for the detection of cathodic copper plate surface quality issues in the copper electrolysis production process. Full article
(This article belongs to the Section Information Applications)
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14 pages, 5073 KiB  
Article
Small Target Detection Algorithm Based on Improved YOLOv5
by Ruiyun Chen, Zhonghua Liu, Weihua Ou and Kaibing Zhang
Electronics 2024, 13(21), 4158; https://doi.org/10.3390/electronics13214158 - 23 Oct 2024
Cited by 3 | Viewed by 1931
Abstract
Small targets exist in large numbers in various fields. They are broadly used in aerospace, video monitoring, and industrial detection. However, because of its tiny dimensions and modest resolution, the precision of small-target detection is low, and the erroneous detection rate is high. [...] Read more.
Small targets exist in large numbers in various fields. They are broadly used in aerospace, video monitoring, and industrial detection. However, because of its tiny dimensions and modest resolution, the precision of small-target detection is low, and the erroneous detection rate is high. Therefore, based on YOLOv5, an improved small-target detection model is proposed. First, in order to improve the number of tiny targets detected while enhancing small-target detection performance, an additional detection head is added. Second, involution is used between the backbone and neck to increase the channel information of feature mapping. Third, the model introduces the BiFormer, wherein both the global and local feature information are captured simultaneously by means of its double-layer routing attention mechanism. Finally, a context augmentation module (CAM) is inserted into the neck in order to maximize the structure of feature fusion. In addition, in order to consider among the required real frame as well as the prediction frame simultaneously, YOLOv5’s original loss function is exchanged. The experimental results using the public dataset VisDrone2019 show that the proposed model has P increased by 13.43%, R increased by 11.28%, and mAP@.5 and mAP@[.5:.95] increased by 13.88% and 9.01%, respectively. Full article
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23 pages, 12865 KiB  
Article
FGYOLO: An Integrated Feature Enhancement Lightweight Unmanned Aerial Vehicle Forest Fire Detection Framework Based on YOLOv8n
by Yangyang Zheng, Fazhan Tao, Zhengyang Gao and Jingyan Li
Forests 2024, 15(10), 1823; https://doi.org/10.3390/f15101823 - 18 Oct 2024
Cited by 5 | Viewed by 1455
Abstract
To address the challenges of complex backgrounds and small, easily confused fire and smoke targets in Unmanned Aerial Vehicle (UAV)-based forest fire detection, we propose an improved forest smoke and fire detection algorithm based on YOLOv8. Considering the limited computational resources of UAVs [...] Read more.
To address the challenges of complex backgrounds and small, easily confused fire and smoke targets in Unmanned Aerial Vehicle (UAV)-based forest fire detection, we propose an improved forest smoke and fire detection algorithm based on YOLOv8. Considering the limited computational resources of UAVs and the lightweight property of YOLOv8n, the original model of YOLOv8n is improved, the Bottleneck module is reconstructed using Group Shuffle Convolution (GSConv), and the residual structure is improved, thereby enhancing the model’s detection capability while reducing network parameters. The GBFPN module is proposed to optimize the neck layer network structure and fusion method, enabling the more effective extraction and fusion of pyrotechnic features. Recognizing the difficulty in capturing the prominent characteristics of fire and smoke in a complex, tree-heavy environment, we implemented the BiFormer attention mechanism to boost the model’s ability to acquire multi-scale properties while retaining fine-grained features. Additionally, the Inner-MPDIoU loss function is implemented to replace the original CIoU loss function, thereby improving the model’s capacity for detecting small targets. The experimental results of the customized G-Fire dataset reveal that FGYOLO achieves a 3.3% improvement in mean Average Precision (mAP), reaching 98.8%, while reducing the number of parameters by 26.4% compared to the original YOLOv8n. Full article
(This article belongs to the Special Issue Wildfire Monitoring and Risk Management in Forests)
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15 pages, 7456 KiB  
Article
A Tomato Recognition and Rapid Sorting System Based on Improved YOLOv10
by Weirui Liu, Su Wang, Xingjun Gao and Hui Yang
Machines 2024, 12(10), 689; https://doi.org/10.3390/machines12100689 - 30 Sep 2024
Cited by 5 | Viewed by 2028
Abstract
In order to address the issue of time-consuming, labor-intensive traditional industrial tomato sorting, this paper proposes a high-precision tomato recognition strategy and fast automatic grasping system. Firstly, the Swin Transformer module is integrated into YOLOv10 to reduce the resolution of each layer by [...] Read more.
In order to address the issue of time-consuming, labor-intensive traditional industrial tomato sorting, this paper proposes a high-precision tomato recognition strategy and fast automatic grasping system. Firstly, the Swin Transformer module is integrated into YOLOv10 to reduce the resolution of each layer by half and double the number of channels, improving recognition accuracy. Then, the Simple Attention Module (SimAM) and the Efficient Multi-Scale Attention (EMA) attention mechanisms are added to achieve complete integration of features, and the Bi-level Routing Attention (BiFormer) is introduced for dynamic sparse attention and resource allocation. Finally, a lightweight detection head is added to YOLOv10 to improve the accuracy of tiny target detection. To complement the recognition system, a single-vertex and multi-crease (SVMC) origami soft gripper is employed for rapid adaptive grasping of identified objects through bistable deformation. This innovative system enables quick and accurate tomato grasping post-identification, showcasing significant potential for application in fruit and vegetable sorting operations. Full article
(This article belongs to the Section Machine Design and Theory)
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19 pages, 14422 KiB  
Article
YOLO-SegNet: A Method for Individual Street Tree Segmentation Based on the Improved YOLOv8 and the SegFormer Network
by Tingting Yang, Suyin Zhou, Aijun Xu, Junhua Ye and Jianxin Yin
Agriculture 2024, 14(9), 1620; https://doi.org/10.3390/agriculture14091620 - 15 Sep 2024
Cited by 3 | Viewed by 2550
Abstract
In urban forest management, individual street tree segmentation is a fundamental method to obtain tree phenotypes, which is especially critical. Most existing tree image segmentation models have been evaluated on smaller datasets and lack experimental verification on larger, publicly available datasets. Therefore, this [...] Read more.
In urban forest management, individual street tree segmentation is a fundamental method to obtain tree phenotypes, which is especially critical. Most existing tree image segmentation models have been evaluated on smaller datasets and lack experimental verification on larger, publicly available datasets. Therefore, this paper, based on a large, publicly available urban street tree dataset, proposes YOLO-SegNet for individual street tree segmentation. In the first stage of the street tree object detection task, the BiFormer attention mechanism was introduced into the YOLOv8 network to increase the contextual information extraction and improve the ability of the network to detect multiscale and multishaped targets. In the second-stage street tree segmentation task, the SegFormer network was proposed to obtain street tree edge information more efficiently. The experimental results indicate that our proposed YOLO-SegNet method, which combines YOLOv8+BiFormer and SegFormer, achieved a 92.0% mean intersection over union (mIoU), 95.9% mean pixel accuracy (mPA), and 97.4% accuracy on a large, publicly available urban street tree dataset. Compared with those of the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPNet), UNet, DeepLabv3+, and HRNet, the mIoUs of our YOLO-SegNet increased by 10.5, 9.7, 5.0, 6.8, 4.5, and 2.7 percentage points, respectively. The proposed method can effectively support smart agroforestry development. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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24 pages, 5578 KiB  
Article
Study on Nighttime Pedestrian Trajectory-Tracking from the Perspective of Driving Blind Spots
by Wei Zhao, Congcong Ren and Ao Tan
Electronics 2024, 13(17), 3460; https://doi.org/10.3390/electronics13173460 - 31 Aug 2024
Cited by 1 | Viewed by 1475
Abstract
With the acceleration of urbanization and the growing demand for traffic safety, developing intelligent systems capable of accurately recognizing and tracking pedestrian trajectories at night or under low-light conditions has become a research focus in the field of transportation. This study aims to [...] Read more.
With the acceleration of urbanization and the growing demand for traffic safety, developing intelligent systems capable of accurately recognizing and tracking pedestrian trajectories at night or under low-light conditions has become a research focus in the field of transportation. This study aims to improve the accuracy and real-time performance of nighttime pedestrian-detection and -tracking. A method that integrates the multi-object detection algorithm YOLOP with the multi-object tracking algorithm DeepSORT is proposed. The improved YOLOP algorithm incorporates the C2f-faster structure in the Backbone and Neck sections, enhancing feature extraction capabilities. Additionally, a BiFormer attention mechanism is introduced to focus on the recognition of small-area features, the CARAFE module is added to improve shallow feature fusion, and the DyHead dynamic target-detection head is employed for comprehensive fusion. In terms of tracking, the ShuffleNetV2 lightweight module is integrated to reduce model parameters and network complexity. Experimental results demonstrate that the proposed FBCD-YOLOP model improves lane detection accuracy by 5.1%, increases the IoU metric by 0.8%, and enhances detection speed by 25 FPS compared to the baseline model. The accuracy of nighttime pedestrian-detection reached 89.6%, representing improvements of 1.3%, 0.9%, and 3.8% over the single-task YOLO v5, multi-task TDL-YOLO, and the original YOLOP models, respectively. These enhancements significantly improve the model’s detection performance in complex nighttime environments. The enhanced DeepSORT algorithm achieved an MOTA of 86.3% and an MOTP of 84.9%, with ID switch occurrences reduced to 5. Compared to the ByteTrack and StrongSORT algorithms, MOTA improved by 2.9% and 0.4%, respectively. Additionally, network parameters were reduced by 63.6%, significantly enhancing the real-time performance of nighttime pedestrian-detection and -tracking, making it highly suitable for deployment on intelligent edge computing surveillance platforms. Full article
(This article belongs to the Section Artificial Intelligence)
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23 pages, 25505 KiB  
Article
A New Method for Non-Destructive Identification and Tracking of Multi-Object Behaviors in Beef Cattle Based on Deep Learning
by Guangbo Li, Jiayong Sun, Manyu Guan, Shuai Sun, Guolong Shi and Changjie Zhu
Animals 2024, 14(17), 2464; https://doi.org/10.3390/ani14172464 - 24 Aug 2024
Cited by 11 | Viewed by 1922
Abstract
The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods [...] Read more.
The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model’s receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm’s capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming. Full article
(This article belongs to the Section Cattle)
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14 pages, 8316 KiB  
Article
Maize Anthesis-Silking Interval Estimation via Image Detection under Field Rail-Based Phenotyping Platform
by Lvhan Zhuang, Chuanyu Wang, Haoyuan Hao, Wei Song and Xinyu Guo
Agronomy 2024, 14(8), 1723; https://doi.org/10.3390/agronomy14081723 - 5 Aug 2024
Cited by 1 | Viewed by 2175
Abstract
The Anthesis-Silking Interval (ASI) is a crucial indicator of the synchrony of reproductive development in maize, reflecting its sensitivity to adverse environmental conditions such as heat stress and drought. This paper presents an automated method for detecting the maize ASI index using a [...] Read more.
The Anthesis-Silking Interval (ASI) is a crucial indicator of the synchrony of reproductive development in maize, reflecting its sensitivity to adverse environmental conditions such as heat stress and drought. This paper presents an automated method for detecting the maize ASI index using a field high-throughput phenotyping platform. Initially, high temporal-resolution visible-light image sequences of maize plants from the tasseling to silking stage are collected using a field rail-based phenotyping platform. Then, the training results of different sizes of YOLOv8 models on this dataset are compared to select the most suitable base model for the task of detecting maize tassels and ear silks. The chosen model is enhanced by incorporating the SENetv2 and the dual-layer routing attention mechanism BiFormer, named SEBi-YOLOv8. The SEBi-YOLOv8 model, with these combined modules, shows improvements of 2.3% and 8.2% in mAP over the original model, reaching 0.989 and 0.886, respectively. Finally, SEBi-YOLOv8 is used for the dynamic detection of maize tassels and ear silks in maize populations. The experimental results demonstrate the method’s high detection accuracy, with a correlation coefficient (R2) of 0.987 and an RMSE of 0.316. Based on these detection results, the ASI indices of different inbred lines are calculated and compared. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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17 pages, 2713 KiB  
Article
Gasoline Engine Misfire Fault Diagnosis Method Based on Improved YOLOv8
by Zhichen Li, Zhao Qin, Weiping Luo and Xiujun Ling
Electronics 2024, 13(14), 2688; https://doi.org/10.3390/electronics13142688 - 9 Jul 2024
Cited by 1 | Viewed by 1397
Abstract
In order to realize the online diagnosis and prediction of gasoline engine fire faults, this paper proposes an improved misfire fault detection algorithm model based on YOLOv8 for sound signals of gasoline engines. The improvement involves substituting a C2f module in the YOLOv8 [...] Read more.
In order to realize the online diagnosis and prediction of gasoline engine fire faults, this paper proposes an improved misfire fault detection algorithm model based on YOLOv8 for sound signals of gasoline engines. The improvement involves substituting a C2f module in the YOLOv8 backbone network by a BiFormer attention module and another C2f module substituted by a CBAM module that combines channel and spatial attention mechanisms which enhance the neural network’s capacity to extract the complex features. The normal and misfire sound signals of a gasoline engine are processed by wavelet transformation and converted to time–frequency images for the training, verification, and testing of convolutional neural network. The experimental results show that the precision of the improved YOLOv8 algorithm model is 99.71% for gasoline engine fire fault tests, which is 2 percentage points higher than for the YOLOv8 network model. The diagnosis time of each sound is less than 100 ms, making it suitable for developing IoT devices for gasoline engine misfire fault diagnosis and driverless vehicles. Full article
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22 pages, 13347 KiB  
Article
Research on Automated Fiber Placement Surface Defect Detection Based on Improved YOLOv7
by Liwei Wen, Shihao Li, Zhentao Dong, Haiqing Shen and Entao Xu
Appl. Sci. 2024, 14(13), 5657; https://doi.org/10.3390/app14135657 - 28 Jun 2024
Cited by 1 | Viewed by 1603
Abstract
Due to the black and glossy appearance of the carbon fiber prepreg bundle surface, the accurate identification of surface defects in automated fiber placement (AFP) presents a high level of difficulty. Currently, the enhanced YOLOv7 algorithm demonstrates certain performance advantages in this detection [...] Read more.
Due to the black and glossy appearance of the carbon fiber prepreg bundle surface, the accurate identification of surface defects in automated fiber placement (AFP) presents a high level of difficulty. Currently, the enhanced YOLOv7 algorithm demonstrates certain performance advantages in this detection task, yet issues with missed detections, false alarms, and low confidence levels persist. Therefore, this study proposes an improved YOLOv7 algorithm to further enhance the performance and generalization of surface defect detection in AFP. Firstly, to enhance the model’s feature extraction capability, the BiFormer attention mechanism is introduced to make the model pay more attention to small target defects, thereby improving feature discriminability. Next, the AFPN structure is used to replace the PAFPN at the neck layer to strengthen feature fusion, preserve semantic information to a greater extent, and finely integrate multi-scale features. Finally, WIoU is adopted to replace CIoU as the bounding box regression loss function, making it more sensitive to small targets, enabling more accurate prediction of object bounding boxes, and enhancing the model’s detection accuracy and generalization capability. Through a series of ablation experiments, the improved YOLOv7 shows a 10.5% increase in mAP and a 14 FPS increase in frame rate, with a maximum detection speed of 35 m/min during the AFP process, meeting the requirements of online detection and thus being able to be applied to surface defect detection in AFP operations. Full article
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16 pages, 4397 KiB  
Article
BPN-YOLO: A Novel Method for Wood Defect Detection Based on YOLOv7
by Rijun Wang, Yesheng Chen, Fulong Liang, Bo Wang, Xiangwei Mou and Guanghao Zhang
Forests 2024, 15(7), 1096; https://doi.org/10.3390/f15071096 - 25 Jun 2024
Cited by 13 | Viewed by 2863
Abstract
The detection of wood defect is a crucial step in wood processing and manufacturing, determining the quality and reliability of wood products. To achieve accurate wood defect detection, a novel method named BPN-YOLO is proposed. The ordinary convolution in the ELAN module of [...] Read more.
The detection of wood defect is a crucial step in wood processing and manufacturing, determining the quality and reliability of wood products. To achieve accurate wood defect detection, a novel method named BPN-YOLO is proposed. The ordinary convolution in the ELAN module of the YOLOv7 backbone network is replaced with Pconv partial convolution, resulting in the P-ELAN module. Wood defect detection performance is improved by this modification while unnecessary redundant computations and memory accesses are reduced. Additionally, the Biformer attention mechanism is introduced to achieve more flexible computation allocation and content awareness. The IOU loss function is replaced with the NWD loss function, addressing the sensitivity of the IOU loss function to small defect location fluctuations. The BPN-YOLO model has been rigorously evaluated using an optimized wood defect dataset, and ablation and comparison experiments have been performed. The experimental results show that the mean average precision (mAP) of BPN-YOLO is improved by 7.4% relative to the original algorithm, which can better meet the need to accurately detecting surface defects on wood. Full article
(This article belongs to the Special Issue Wood Quality and Wood Processing)
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