Research on Detection and Counting Method of Green Walnut Based on YOLOv8n-RBP
In natural environments, green walnuts often experience occlusion by branches and leaves, fruit overlap, and varying lighting conditions. To address the issues of low detection accuracy, missed detections, and false positives in the YOLO model, this study proposes an improved YOLOv8n-based detection...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10906562/ |
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| author | Bangbang Chen Keke Tan Kun Li Baojian Ma Xiangdong Liu |
| author_facet | Bangbang Chen Keke Tan Kun Li Baojian Ma Xiangdong Liu |
| author_sort | Bangbang Chen |
| collection | DOAJ |
| description | In natural environments, green walnuts often experience occlusion by branches and leaves, fruit overlap, and varying lighting conditions. To address the issues of low detection accuracy, missed detections, and false positives in the YOLO model, this study proposes an improved YOLOv8n-based detection and counting model for green walnuts, named YOLOv8n-RBP. First, a receptive field-concentrated attention module (RFCBAM) is integrated into the backbone network to enhance feature extraction capabilities. Second, a BiFPN-GLSA module is introduced to replace the Path Aggregation Network (PANet) in the neck, improving the fusion of feature layers from the backbone and Neck networks and enhancing the model’s ability to capture both global and local spatial features. Lastly, to address the weak generalization and slow convergence issues of the CIoU loss function in detection tasks, the PIoUv2 loss function is employed to accelerate bounding box regression and improve detection performance. Experimental results demonstrate that the YOLOv8n-RBP model excels across multiple evaluation metrics. Specifically, the model achieves a mean average precision (mAP@0.5) of 82.2% and a recall rate of 72.4%, with a model size of only 4.65 MB, 2.2 million parameters, and 8.3 GFLOPs. Compared to the original YOLOv8n model, the recall rate and mAP@0.5 improve by 2.7% and 2.5%, respectively, while the number of parameters, FLOPs, and model size decrease by 26.7%, 0.6%, and 22.0%, respectively. Further deployment on the NVIDIA Jetson Xavier NX demonstrates the model’s robust performance under natural conditions, indicating its suitability for orchard operations. |
| format | Article |
| id | doaj-art-5f74df047e944a05800c9d7c2c824be7 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-5f74df047e944a05800c9d7c2c824be72025-08-20T02:59:45ZengIEEEIEEE Access2169-35362025-01-0113392753928810.1109/ACCESS.2025.354631310906562Research on Detection and Counting Method of Green Walnut Based on YOLOv8n-RBPBangbang Chen0https://orcid.org/0009-0009-2110-6393Keke Tan1Kun Li2Baojian Ma3Xiangdong Liu4School of Mechatronic Engineering, Xinjiang Institute of Technology, Aksu, ChinaSchool of Mechatronic Engineering, Xinjiang Institute of Technology, Aksu, ChinaSchool of Mechatronic Engineering, Xinjiang Institute of Technology, Aksu, ChinaSchool of Mechatronic Engineering, Xinjiang Institute of Technology, Aksu, ChinaSchool of Mechatronic Engineering, Xinjiang Institute of Technology, Aksu, ChinaIn natural environments, green walnuts often experience occlusion by branches and leaves, fruit overlap, and varying lighting conditions. To address the issues of low detection accuracy, missed detections, and false positives in the YOLO model, this study proposes an improved YOLOv8n-based detection and counting model for green walnuts, named YOLOv8n-RBP. First, a receptive field-concentrated attention module (RFCBAM) is integrated into the backbone network to enhance feature extraction capabilities. Second, a BiFPN-GLSA module is introduced to replace the Path Aggregation Network (PANet) in the neck, improving the fusion of feature layers from the backbone and Neck networks and enhancing the model’s ability to capture both global and local spatial features. Lastly, to address the weak generalization and slow convergence issues of the CIoU loss function in detection tasks, the PIoUv2 loss function is employed to accelerate bounding box regression and improve detection performance. Experimental results demonstrate that the YOLOv8n-RBP model excels across multiple evaluation metrics. Specifically, the model achieves a mean average precision (mAP@0.5) of 82.2% and a recall rate of 72.4%, with a model size of only 4.65 MB, 2.2 million parameters, and 8.3 GFLOPs. Compared to the original YOLOv8n model, the recall rate and mAP@0.5 improve by 2.7% and 2.5%, respectively, while the number of parameters, FLOPs, and model size decrease by 26.7%, 0.6%, and 22.0%, respectively. Further deployment on the NVIDIA Jetson Xavier NX demonstrates the model’s robust performance under natural conditions, indicating its suitability for orchard operations.https://ieeexplore.ieee.org/document/10906562/Natural environmentgreen walnutdetectionalgorithm improvementequipment deployment |
| spellingShingle | Bangbang Chen Keke Tan Kun Li Baojian Ma Xiangdong Liu Research on Detection and Counting Method of Green Walnut Based on YOLOv8n-RBP IEEE Access Natural environment green walnut detection algorithm improvement equipment deployment |
| title | Research on Detection and Counting Method of Green Walnut Based on YOLOv8n-RBP |
| title_full | Research on Detection and Counting Method of Green Walnut Based on YOLOv8n-RBP |
| title_fullStr | Research on Detection and Counting Method of Green Walnut Based on YOLOv8n-RBP |
| title_full_unstemmed | Research on Detection and Counting Method of Green Walnut Based on YOLOv8n-RBP |
| title_short | Research on Detection and Counting Method of Green Walnut Based on YOLOv8n-RBP |
| title_sort | research on detection and counting method of green walnut based on yolov8n rbp |
| topic | Natural environment green walnut detection algorithm improvement equipment deployment |
| url | https://ieeexplore.ieee.org/document/10906562/ |
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