改进YOLOv5s的落叶树鸟巢检测方法OACSTPCD

To address the difficulty of detecting small bird nest targets in complex backgrounds,an improved YOLOv5s network architecture named YOLOv5s-nest is proposed.YOLOv5s-nest incorporates several enhancements:a refined attention mecha-nism called Bi-CBAM is inserted into the Backbone to effectively enhance the network's perception of small targets;the SDI structure is introduced into the Neck to integrate more hierarchical feature maps and higher-level semantic information;the In-ceptionNeXt structure is inserted into the Neck to improve the model's performance and computational efficiency;and in the de-tection head,ordinary convolutions are replaced with PConv to efficiently extract spatial features and enhance detection effi-ciency.The experimental results show that the average precision of the improved model reached 89.1%,representing an increase of 6.8 percentage points compared to the original model.