Surface defect detection of fiber placement based on virtual sample generation

Automated Fiber Placement (AFP) components are affected by environmental and technological factors in the actual production process, leading to the generation of defects and posing serious harm to the fiber placement components and the overall assembly. Due to the confidentiality of AFP and forming technology, as well as the expensive and time-consuming manufacturing process, it is difficult to obtain sample datasets related to defect images, resulting in the problem of small sample learning. This article proposes a method of expanding the dataset through virtual sample generation and using it as input training samples for the target detection network. In response to the characteristics of fiber placement defects, a coordinate attention mechanism was added to YOLOv5s to optimize the design. The final target detection network model was named Place YOLO, with an accuracy value of 0.941 and a mean Average Precision (mAP) of 0.833, which is 1.8% higher than that of the original YOLOv5s.