BGA-YOLOX-s: Real-time fine-grained detection of silkworm cocoon defects with a ghost convolution module and a joint multiscale fusion attention mechanism

Abstract

The study addresses deficiencies in silkworm cocoon defect detection, enhancing the YOLOX-s network with the BGA-YOLOX-s model. By incorporating BiFPN-m, it reduces feature information loss, improving model reasoning speed. Ghost convolution reduces complexity and parameters, decreasing computational expenses. An attention module (CA) enhances fine-grained feature extraction. Experimental results on a cocoon dataset reveal a 4.1 % accuracy boost to 94.89 % compared to YOLOX-s. Furthermore, BGA-YOLOX-s outperforms SSD, YOLOv3, YOLOv4, and YOLOv5 in defect detection. The model proves effective in online cocoon defect detection, offering guidance for future applications in the production process.