Improved method for a pedestrian detection model based on YOLO
To address the dual challenges of excessive energy consumption and operational inefficiency inherent in the reliance of current agricultural machinery on direct supervision, this study developed an enhanced YOLOv8n-SS pedestrian detection algorithm through architectural modifications to the baseline...
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| Format: | Article |
| Language: | English |
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Higher Education Press
2025-06-01
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| Series: | Frontiers of Agricultural Science and Engineering |
| Subjects: | |
| Online Access: | https://journal.hep.com.cn/fase/EN/PDF/10.15302/J-FASE-2025613 |
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| _version_ | 1850149094122586112 |
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| author | Yanfei LI Chengyi DONG |
| author_facet | Yanfei LI Chengyi DONG |
| author_sort | Yanfei LI |
| collection | DOAJ |
| description | To address the dual challenges of excessive energy consumption and operational inefficiency inherent in the reliance of current agricultural machinery on direct supervision, this study developed an enhanced YOLOv8n-SS pedestrian detection algorithm through architectural modifications to the baseline YOLOv8n framework. The proposed method had superior performance in dense agricultural contexts while improving detection capabilities for pedestrian distribution patterns under complex farmland conditions, including variable lighting and mechanical occlusions. The main innovations were: (1) integration of spatial pyramid dilated (SPD) operations with conventional convolution layers to construct SPD-Conv modules, which effectively mitigated feature information loss while enhancing small-target detection accuracy; (2) incorporation of selective kernel attention mechanisms to enable context-aware feature selection and adaptive feature extraction. Experimental validation revealed significant performance improvements over the original YOLOv8n model. This enhanced architecture achieved 7.2% and 9.2% increases in mAP0.5 and mAP0.5:0.95 metrics respectively for dense pedestrian detection, with corresponding improvements of 7.6% and 8.7% observed in actual farmland working environments. The proposed method ultimately provides a computationally efficient and robust intelligent monitoring solution for agricultural mechanization, facilitating the transition from conventional agricultural practices toward sustainable, low-carbon production paradigms through algorithmic optimization. |
| format | Article |
| id | doaj-art-ffe1989a58c44542b479efb2b1e3d160 |
| institution | OA Journals |
| issn | 2095-7505 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Higher Education Press |
| record_format | Article |
| series | Frontiers of Agricultural Science and Engineering |
| spelling | doaj-art-ffe1989a58c44542b479efb2b1e3d1602025-08-20T02:27:01ZengHigher Education PressFrontiers of Agricultural Science and Engineering2095-75052025-06-0112224526010.15302/J-FASE-2025613Improved method for a pedestrian detection model based on YOLOYanfei LIChengyi DONGTo address the dual challenges of excessive energy consumption and operational inefficiency inherent in the reliance of current agricultural machinery on direct supervision, this study developed an enhanced YOLOv8n-SS pedestrian detection algorithm through architectural modifications to the baseline YOLOv8n framework. The proposed method had superior performance in dense agricultural contexts while improving detection capabilities for pedestrian distribution patterns under complex farmland conditions, including variable lighting and mechanical occlusions. The main innovations were: (1) integration of spatial pyramid dilated (SPD) operations with conventional convolution layers to construct SPD-Conv modules, which effectively mitigated feature information loss while enhancing small-target detection accuracy; (2) incorporation of selective kernel attention mechanisms to enable context-aware feature selection and adaptive feature extraction. Experimental validation revealed significant performance improvements over the original YOLOv8n model. This enhanced architecture achieved 7.2% and 9.2% increases in mAP0.5 and mAP0.5:0.95 metrics respectively for dense pedestrian detection, with corresponding improvements of 7.6% and 8.7% observed in actual farmland working environments. The proposed method ultimately provides a computationally efficient and robust intelligent monitoring solution for agricultural mechanization, facilitating the transition from conventional agricultural practices toward sustainable, low-carbon production paradigms through algorithmic optimization.https://journal.hep.com.cn/fase/EN/PDF/10.15302/J-FASE-2025613YOLOv8ndense pedestrian detectionSPD-ConvSK attention mechanismadaptive extraction |
| spellingShingle | Yanfei LI Chengyi DONG Improved method for a pedestrian detection model based on YOLO Frontiers of Agricultural Science and Engineering YOLOv8n dense pedestrian detection SPD-Conv SK attention mechanism adaptive extraction |
| title | Improved method for a pedestrian detection model based on YOLO |
| title_full | Improved method for a pedestrian detection model based on YOLO |
| title_fullStr | Improved method for a pedestrian detection model based on YOLO |
| title_full_unstemmed | Improved method for a pedestrian detection model based on YOLO |
| title_short | Improved method for a pedestrian detection model based on YOLO |
| title_sort | improved method for a pedestrian detection model based on yolo |
| topic | YOLOv8n dense pedestrian detection SPD-Conv SK attention mechanism adaptive extraction |
| url | https://journal.hep.com.cn/fase/EN/PDF/10.15302/J-FASE-2025613 |
| work_keys_str_mv | AT yanfeili improvedmethodforapedestriandetectionmodelbasedonyolo AT chengyidong improvedmethodforapedestriandetectionmodelbasedonyolo |