面向失效增强和改进YOLOv8的目标检测OA

To address the issues of low detection performance and weak generalization ability in target detection under complex background conditions such as illumination,weather,and occlusion,this paper proposes an improved object detection algorithm based on failure augmentation and enhanced YOLOv8(AS_YOLO).First,a variety of target unit datasets were constructed based on complex military scenarios,and an image failure augmentation technique tailored to the application environment was developed.Second,a channel-spatial parallel attention mechanism was introduced to simultaneously focus on feature and position information of targets in complex environments.Then,the AFPN structure was used to enhance feature fusion of non-adjacent hierarchical layers.Finally,the Inner_IoU loss function was adopted to address the generalization limitations of existing IoU loss functions in different detection tasks.Transfer experiments were conducted on the WSODD multi-target dataset.The experimental results show that the improved algorithm achieves an mAP0.5 of 94.0％,a 12.5 percentage point improvement over the baseline YOLOv8n model,and an mAP0.95 of 72.5％,a 15.7 percentage point improvement,indicating superior detection performance.