PC-Yolo: enhanced YOLOv5-based defect detection system with improved partial convolution for ham sausage inspection

Advances in automation for the packaging industry necessitate robust quality control measures, particularly in food appearance inspection. This paper introduces PC-Yolo, an advanced defect detection system based on an enhanced version of the YOLOv5 algorithm, tailored for the inspection of ham sausage products. PC-Yolo incorporates Partial Convolution (PConv) to improve the defect detection process, offering substantial gains in speed and accuracy over traditional methods. A custom dataset comprising high-resolution images of ham sausages, with and without defects, was used to train and validate the model. The proposed system efficiently handles variations in defect size through optimal prior box dimensionality and employs a combination of Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) for effective feature fusion. The resultant PC-Yolo model demonstrates superior real-time detection capabilities, with robust performance in complex scenarios, thereby addressing the industry's zero-defect challenge.