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 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.