Application of YOLOv8 Architecture Optimized based on BiFPN in Leather Defect Recognition

Traditional image processing methods are difficult to effectively deal with complex backgrounds and defects with different scales. This paper proposed a YOLOv8 architecture optimization strategy that integrates Bidirectional Feature Pyramid Network (BiFPN) to improve the accuracy and efficiency of leather defect recognition. YOLOv8, as an efficient object detection framework, combined the multi-scale feature fusion advantages of BiFPN to enhance the model's feature extraction ability in complex backgrounds. By introducing the BiFPN module in YOLOv8, the model can better capture leather defects with different scales, and further improve the accuracy and stability of recognition through the optimized loss functions. The experimental results showed that the weight of YOLOv8 before improvement was 6.3 MB, which decreased to 4.3 MB after improvement, and mAP50 increased by 0.2%. This optimization strategy improves recognition accuracy and speed compared to traditional methods and YOLOv8 without BiFPN fusion, and optimizes the effectiveness and practical application potential of the YOLOv8 framework in leather defect detection.