YOLO-FDD: efficient defect detection network of aircraft skin fastener

Fasteners defects in aircraft skin can seriously threaten the operational safeness of the aircraft. Therefore, the periodic detection of defects on aircraft skin fasteners is necessary. Due to the small size of aircraft skin fastener defects, traditional defect detection algorithms have lower detection precision. Therefore, we propose YOLO-FDD, an effective network for detecting aircraft skin fastener defects. Firstly, four detection layers and residual spatial pyramid pooling (R-SPP) module are used in the network. Secondly, an attention fusion feature pyramid networks (AF-FPN) is designed to adaptively fuse features and purify semantic conflicts. In addition, the attention head with swin transformer block (STB) and coordinate attention (CA) is designed as the feature detection module to enhance the location precision of defects. Finally, a dataset comprising of four types of aircraft skin fastener defects is constructed. According to the experimental findings, YOLO-FDD achieved a mAP of 83.08%, and the detection speed is 30.44 FPS, the YOLO-FDD network has great potential for application in aircraft skin fastener defect detection.