基于YOLOv8的林区行人目标检测研究OA北大核心

In order to solve the problem that the target detection algorithm is prone to leakage detection and insufficient detection accuracy in pedestrian detection in forest areas,a forest pedestrian target detection algorithm based on improved YOLOv8 is pro-posed.The C2f_DWRSeg module is used to replace the C2f module,and the number of initial convolutional channels is expanded so that the network can extract multi-scale features more efficiently.A reconstructed detector head is proposed to increase the complexity of the convolution layer during training,and a single branch structure is used in inference,so as to enrich the feature representation of the network and maintain efficient inference speed;add CGA,the convolution attention mechanism module,before feature fusion,to reduce the amount of calculation;use the Focaler-ShapeIoU loss function to replace the CIoU loss function to make up for the short-comings of the boundary box regression method and further improve the detection ability.Experimental results show that compared with benchmark model,the improved algorithm mAP50 has increased by 2%,mAP50-95 has increased by 2.4%,and FPS has in-creased by 4.33%.It proves that the improved algorithm can be better applied to the task of pedestrian detection in forest areas.