Search Results (19)

This study addresses the challenge of instance segmentation for key grapevine structures (Trunk, Branch, and Bud) in complex natural environments, focusing on issues such as varying light conditions, weather, and significant scale variations. We propose an enhanced instance segmentation model named CDA-YOLOv8. Trained on a self-built dataset of 2160 images covering grapevine scenes under diverse lighting conditions, this model integrates three key components: the ACmix module for enhanced global feature modeling, the C2f-DWR module for optimized multi-scale feature extraction, and the CSPPC module for achieving model lightweighting. We evaluate performance using precision/recall and mAP@50, together with the stricter mAP@[50:95], for segmentation quality, and parameters/model size/FPS for deployment efficiency. Experimental results demonstrate that CDA-YOLOv8 achieves 70.1% precision, 74.4% recall, 76.3% mAP@50, and 36.8% mAP@[50:95], with only 3.19 million parameters and a compact model size of 6.49 MB. Compared with the original YOLOv8-seg, CDA-YOLOv8 improves segmentation accuracy while maintaining high efficiency (6.87 FPS). It also delivers better mask quality under stricter overlap criteria, providing quantitative evidence for real-time perception in automated grapevine pruning systems. Full article

An accurate and effective peach recognition algorithm is a key part of automated picking in orchards; however, the current peach recognition algorithms are mainly targeted at bare fruit scenarios and face challenges in recognizing flat peach bagged fruits, based on which this paper proposes a model for recognizing and detecting flat peach fruits in complex orchard environments after bagging, namely, YOLOv8n-CDDSh. First, to effectively deal with the problem of the insufficient detection capability of small targets in orchard environments, the dilation-wise residual (DWR) module is introduced to enhance the model’s understanding of semantic information about small target defects. Second, in order to improve the detection ability in complex occlusion scenarios, inspired by the idea of large kernel convolution and cavity convolution in the Dilated Reparam Block (DRB) module, the C2f-DWR-DRB architecture is built to improve the detection ability in occluded target regions. Thirdly, in order to improve the sensitivity and precision of aspect ratio optimization, and to better adapt to the detection scenarios of targets with large differences in shapes, the ShapeIoU loss function is used to improve the fruit localization precision. Finally, we validate the effectiveness of the proposed method through experiments conducted on a self-constructed dataset comprising 1089 samples. The results show that the YOLOv8n-CDDSh model achieves 92.1% precision (P), 91.7% Mean Average Precision (mAP), and a model size of 5.73 MB, with improvements of +1.5 pp (Precision) and +0.5 pp (mAP) over YOLOv8n, respectively. In addition, the detection performance is excellent in actual orchard environments with different light angles, shading conditions, and shooting distances. Meanwhile, YOLOv8n-CDDSh deployed on the edge computing device achieved precision = 87.04%, mAP = 91.71%, and FPS = 37.20, and can also maintain high precision in bagged fruit recognition under extreme weather simulations such as fog and rainstorms, providing theoretical and methodological support for the automated picking of bagged peaches. Full article

Agricultural robots operating in greenhouse environments face substantial challenges in detecting tomato stems, including fluctuating lighting, cluttered backgrounds, and the stems’ inherently slender morphology. This study introduces EfficientV1-C2fDWR-IRMB-YOLO (EDI-YOLO), an enhanced model built on YOLOv8n-seg. First, the original backbone is replaced with EfficientNetV1, yielding a 2.3% increase in mAP50 and a 2.6 G reduction in FLOPs. Second, we design a C2f-DWR module that integrates multi-branch dilations with residual connections, enlarging the receptive field and strengthening long-range dependencies; this improves slender-object segmentation by 1.4%. Third, an Inverted Residual Mobile Block (iRMB) is inserted into the neck to apply spatial attention and dual residual paths, boosting key-feature extraction by 1.5% with only +0.7GFLOPs. On a custom tomato-stem dataset, EDI-YOLO achieves 79.3% mAP50 and 33.9% mAP50-95, outperforming the baseline YOLOv8n-seg (75.1%, 31.4%) by 4.2% and 2.6%, and YOLOv5s-seg (66.7%), YOLOv7tiny-seg (75.4%), and YOLOv12s-seg (75.4%) by 12.6%, 3.9%, and 3.9% in mAP50, respectively. Significant improvement is achieved in lateral branch segmentation (60.4% → 65.2%). Running at 86.2 FPS with only 10.4GFLOPs and 8.0 M parameters, EDI-YOLO demonstrates an optimal trade-off between accuracy and efficiency. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

To address the limitations in multi-scale feature processing and illumination sensitivity of existing steel surface defect detection algorithms, we proposed ADP-YOLOv8-n, enhancing accuracy and computational efficiency through advanced feature fusion and optimized network architecture. Firstly, an adaptive weighted down-sampling (ADSConv) module was proposed, which improves detector adaptability to diverse defects via the weighted fusion of down-sampled feature maps. Next, the C2f_DWR module was proposed, integrating optimized C2F architecture with a streamlined DWR design to enhance feature extraction efficiency while reducing computational complexity. Then, a Multi-Scale-Focus Diffusion Pyramid was designed to adaptively handle multi-scale object detection by dynamically adjusting feature fusion, thus reducing feature redundancy and information loss while maintaining a balance between detailed and global information. Experiments demonstrate that the proposed ADP-YOLOv8-n detection algorithm achieves superior performance, effectively balancing detection accuracy, inference speed, and model compactness. Full article

Accurate detection of cotton pests and diseases is essential for agricultural productivity yet remains challenging due to complex field environments, the small size of pests and diseases, and significant occlusions. To address the challenges presented by these factors, a novel cotton disease and pest detection method is proposed. This method builds upon the YOLOv8 baseline model and incorporates a Multi-Scale Sliding Window Attention Module (MSFE) within the backbone architecture to enhance feature extraction capabilities specifically for small targets. Furthermore, a Depth-Separable Dilated Convolution Module (C2f-DWR) is designed to replace the existing C2f module in the neck of the network. By employing varying dilation rates, this modification effectively expands the receptive field and alleviates the loss of detailed information associated with the downsampling processes. In addition, a Multi-Head Attention Detection Head (MultiSEAMDetect) is introduced to supplant the original detection head. This new head utilizes diverse patch sizes alongside adaptive average pooling mechanisms, thereby enabling the model to adjust its responses in accordance with varying contextual scenarios, which significantly enhances its ability to manage occlusion during detection. For the purpose of experimental validation, a dedicated dataset for cotton disease and pest detection was developed. In this dataset, the improved model’s mAP50 and mAP50:95 increased from 73.4% and 46.2% to 77.2% and 48.6%, respectively, compared to the original YOLOv8 algorithm. Validation on two Kaggle datasets showed that mAP50 rose from 92.1% and 97.6% to 93.2% and 97.9%, respectively. Meanwhile, mAP50:95 improved from 86% and 92.5% to 87.1% and 93.5%. These findings provide compelling evidence of the superiority of the proposed algorithm. Compared to other advanced mainstream algorithms, it exhibits higher accuracy and recall, indicating that the improved algorithm performs better in the task of cotton pest and disease detection. Full article

To address challenges of background interference and limited multi-scale feature extraction in infrared small target detection, this paper proposes a YOLO-HVS detection algorithm inspired by the human visual system. Based on YOLOv8, we design a multi-scale spatially enhanced attention module (MultiSEAM) using multi-branch depth-separable convolution to suppress background noise and enhance occluded targets, integrating local details and global context. Meanwhile, the C2f_DWR (dilation-wise residual) module with regional-semantic dual residual structure is designed to significantly improve the efficiency of capturing multi-scale contextual information by expanding convolution and two-step feature extraction mechanism. We construct the DroneRoadVehicles dataset containing 1028 infrared images captured at 70–300 m, covering complex occlusion and multi-scale targets. Experiments show that YOLO-HVS achieves mAP50 of 83.4% and 97.8% on the public dataset DroneVehicle and the self-built dataset, respectively, which is an improvement of 1.1% and 0.7% over the baseline YOLOv8, and the number of model parameters only increases by 2.3 M, and the increase of GFLOPs is controlled at 0.1 G. The experimental results demonstrate that the proposed approach exhibits enhanced robustness in detecting targets under severe occlusion and low SNR conditions, while enabling efficient real-time infrared small target detection. Full article

The growing popularity of solar panels is crucial for global decarbonization, but harsh environmental conditions can lead to defects such as cracks, fingerprints, and short circuits. Existing methods face the challenge of detecting multi-scale defects while maintaining real-time performance. This paper proposes a solar panel defect detection model, DCE-YOLO, based on YOLOv8. The model incorporates a C2f-DWR-DRB module for multi-scale feature extraction, where the parallel DRB branch models spatial symmetry through symmetric-rate dilated convolutions, improving robustness and consistency. The COT attention module strengthens long-range dependencies and fuses local and global contexts to achieve symmetric feature representation. The lightweight and efficient detection head improves detection speed and accuracy. The CIoU loss function is replaced with WIoU, and a non-monotonic dynamic focusing mechanism is used to mitigate the effect of low-quality samples. Experimental results show that compared with the YOLOv8 benchmark, DCE-YOLO achieves a 2.1% performance improvement on mAP@50 and a 4.9% performance improvement on mAP@50-95. Compared with recent methods, DCE-YOLO exhibits broader defect coverage, stronger robustness, and a better performance-efficiency balance, making it highly suitable for edge deployment. The synergistic interaction between the C2f-DWR-DRB module and COT attention enhances the detection of symmetric and multi-scale defects under real-world conditions. Full article

The damaged and deformed fruits of fresh berries severely restrict the economic value of produce, and accurate identification and grading methods have become a global research hotspot. To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this study proposes an enhanced YOLOv8n-based framework for sweet cherry defect identification. First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. This enhances the recognition accuracy of subtle surface defects and large-scale damages on cherries. Second, a channel attention feature fusion mechanism (CAFM) is incorporated at the front end of the detection head, which enhances the model’s ability to identify fine defects on the cherry surface. Additionally, to improve bounding box regression accuracy, powerful-IoU (PIoU) replaces the traditional CIoU loss function. Finally, self-distillation technology is introduced to further improve the mode’s generalization capability and detection accuracy through knowledge transfer. Experimental results show that the YOLOv8-DCPF model achieves precision, mAP, recall, and F1 score rates of 92.6%, 91.2%, 89.4%, and 89.0%, respectively, representing improvements of 6.9%, 5.6%, 6.1%, and 5.0% over the original YOLOv8n baseline network. The proposed model demonstrates high accuracy in cherry defect detection, providing an efficient and precise solution for intelligent cherry sorting in agricultural engineering applications. Full article

To address the multi-target detection problem in the automatic seedling-feeding procedure of vegetable-grafting robots from dual perspectives (top-view and side-view), this paper proposes an improved YOLOv8-SDC detection segmentation model based on YOLOv8n-seg. The model improves rootstock seedlings’ detection and segmentation accuracy by SAConv replacing the original Conv c2f_DWRSeg module, replacing the c2f module, and adding the CA mechanism. Specifically, the SAConv module dynamically adjusts the receptive field of convolutional kernels to enhance the model’s capability in extracting seedling shape features. Additionally, the DWR module enables the network to more flexibly adapt to the perception accuracy of different cotyledons, growth points, stem edges, and contours. Furthermore, the incorporated CA mechanism helps the model eliminate background interference for better localization and identification of seedling grafting characteristics. The improved model was trained and validated using preprocessed data. The experimental results show that YOLOv8-SDC achieves significant accuracy improvements over the original YOLOv8n-seg model, YOLACT, Mask R-CNN, YOLOv5, and YOLOv11 in both object detection and instance segmentation tasks under top-view and side-view conditions. The mAP of Box and Mask for cotyledon (leaf1, leaf2, leaf), growing point (pot), and seedling stem (stem) assays reached 98.6% and 99.1%, respectively. The processing speed reached 200 FPS. The feasibility of the proposed method was further validated through grafting features, such as cotyledon deflection angles and stem–cotyledon separation points. These findings provide robust technical support for developing an automatic seedling-feeding mechanism in grafting robotics. Full article

In order to achieve accurate and rapid identification of red raspberry fruits in the complex environments of fields and greenhouses, this study proposes a new red raspberry maturity detection model based on YOLOv11n. First, the proposed hybrid attention mechanism HCSA (halo attention with channel and spatial attention modules) is embedded in the neck of the YOLOv11n network. This mechanism integrates halo, channel, and spatial attention to enhance feature extraction and representation in fruit detection and improve attention to spatial and channel information. Secondly, dilation-wise residual (DWR) is fused with the C3k2 module of the network and applied to the entire network structure to enhance feature extraction, multi-scale perception, and computational efficiency in red raspberry detection. Concurrently, the DWR module optimizes the learning process through residual connections, thereby enhancing the accuracy and real-time performance of the model. Finally, a lightweight and efficient dynamic upsampling module (DySample) is introduced between the backbone and neck of the network. This module enhances the network’s multi-scale feature extraction capabilities, reduces the interference of background noise, improves the recognition of structural details, and optimizes the spatial resolution of the image through the dynamic sampling mechanism. Reducing network parameters helps the model better capture the maturity characteristics of red raspberry fruits. Experiments were conducted on a custom-built 3167-image dataset of red raspberries, and the results demonstrated that the enhanced YOLOv11n model attained a precision of 0.922, mAP@0.5 of 0.925, and mAP@0.5 of 0.943, respectively, representing improvements of 0.7%, 4.4%, and 4.4%, respectively. At 3.4%, mAP@0.5-0.95 was 0.798, which was 2.0%, 9.8% and 3.7% higher than the original YOLOv11n model, respectively. The mAP@0.5 of unripe and ripe berries was 0.925 and 0.943, which was improved by 0.7% and 4.4%, respectively. The F1-score was enhanced to 0.89, while the computational complexity of the model was only 8.2 GFLOPs, thereby achieving a favorable balance between accuracy and efficiency. This research provides new technical support for precision agriculture and intelligent robotic harvesting. Full article

The acquisition of approaching vessels’ information is a critical technological challenge for maritime risk warning and intelligent collision avoidance decision-making. This paper proposes a method for autonomously identifying types of approaching vessels based on an improved YOLOv8 model and ship light features, aiming to infer the propulsion mode, size, movement, and operational nature of the approaching vessels in real-time through the color, quantity, and spatial distribution of lights. Firstly, to address the challenges of the small target characteristics of ship lights and complex environmental interference, an improved YOLOv8 model is developed: The dilation-wise residual (DWR) module is introduced to optimize the feature extraction capability of the C2f structure. The bidirectional feature pyramid network (BiFPN) is adopted to enhance multi-scale feature fusion. A hybrid attention transformer (HAT) is employed to enhance the small target detection capability of the detection head. This framework achieves precise ship light recognition under complex maritime circumstances. Secondly, 23 spatio-semantic feature indicators are established to encode ship light patterns, and a multi-viewing angle dataset is constructed. This dataset covers 36 vessel types under four viewing angles (front, port-side, starboard, and stern viewing angles), including the color, quantity, combinations, and spatial distribution of the ship lights. Finally, a two-stage discriminative model is proposed: ECA-1D-CNN is utilized for the rapid assessment of the viewing angle of the vessel. Deep learning algorithms are dynamically applied for vessel type determination within the assessed viewing angles. Experimental results show that this method achieves high determination accuracy. This paper provides a kind of technical support for intelligent situational awareness and the autonomous collision avoidance of ships. Full article

Unmanned aerial vehicle (UAV) imagery often suffers from significant object scale variations, high target density, and varying distances due to shooting conditions and environmental factors, leading to reduced robustness and low detection accuracy in conventional models. To address these issues, this study adopts DGBL-YOLOv8s, an improved object detection model tailored for UAV perspectives based on YOLOv8s. First, a Dilated Wide Residual (DWR) module is introduced to replace the C2f module in the backbone network of YOLOv8, enhancing the model’s capability to capture fine-grained features and contextual information. Second, the neck structure is redesigned by incorporating a Global-to-Local Spatial Aggregation (GLSA) module combined with a Bidirectional Feature Pyramid Network (BiFPN), which strengthens feature fusion. Third, a lightweight shared convolution detection head is proposed, incorporating shared convolution and batch normalization techniques. Additionally, to further improve small object detection, a dedicated small-object detection head is introduced. Results from experiments on the VisDrone dataset reveal that DGBL-YOLOv8s enhances detection accuracy by 8.5% relative to the baseline model, alongside a 34.8% reduction in parameter count. The overall performance exceeds most of the current detection models, which confirms the advantages of the proposed improvement. Full article

The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared imagery, we propose an enhanced infrared detection model called LFIR-YOLO, which is built upon the YOLOv8 architecture. The primary goal is to improve the accuracy of infrared target detection in nighttime traffic scenarios while meeting practical deployment requirements. First, to address challenges such as limited contrast and occlusion noise in infrared images, the C2f module in the high-level backbone network is augmented with a Dilation-wise Residual (DWR) module, incorporating multi-scale infrared contextual information to enhance feature extraction capabilities. Secondly, at the neck of the network, a Content-guided Attention (CGA) mechanism is applied to fuse features and re-modulate both initial and advanced features, catering to the low signal-to-noise ratio and sparse detail features characteristic of infrared images. Third, a shared convolution strategy is employed in the detection head, replacing the decoupled head strategy and utilizing shared Detail Enhancement Convolution (DEConv) and Group Norm (GN) operations to achieve lightweight yet precise improvements. Finally, loss functions, PIoU v2 and Adaptive Threshold Focal Loss (ATFL), are integrated into the model to better decouple infrared targets from the background and to enhance convergence speed. The experimental results on the FLIR and multispectral datasets show that the proposed LFIR-YOLO model achieves an improvement in detection accuracy of 4.3% and 2.6%, respectively, compared to the YOLOv8 model. Furthermore, the model demonstrates a reduction in parameters and computational complexity by 15.5% and 34%, respectively, enhancing its suitability for real-time deployment on resource-constrained edge devices. Full article

Strip steel surface defect detection has become a crucial step in ensuring the quality of strip steel production. To address the issues of low detection accuracy and long detection times in strip steel surface defect detection algorithms caused by varying defect sizes and blurred images during acquisition, this paper proposes a lightweight strip steel surface defect detection network, YOLO-SDS, based on an improved YOLOv8. Firstly, StarNet is utilized to replace the backbone network of YOLOv8, achieving lightweight optimization while maintaining accuracy. Secondly, a lightweight module DWR is introduced into the neck and combined with the C2f feature extraction module to enhance the model’s multi-scale feature extraction capability. Finally, an occlusion-aware attention mechanism SEAM is incorporated into the detection head, enabling the model to better capture and process features of occluded objects, thus improving performance in complex scenarios. Experimental results on the open-source NEU-DET dataset show that the improved model reduces parameters by 34.4% compared with the original YOLOv8 algorithm while increasing average detection accuracy by 1.5%. And it shows good generalization performance on the deepPCB dataset. Compared with other defect detection models, YOLO-SDS offers significant advantages in terms of parameter count and detection speed. Additionally, ablation experiments validate the effectiveness of each module. Full article