Mug Defect Detection Method Based on Improved Faster RCNN

Faster RCNN has poorer performance in terms of accuracy and robustness for detecting small targets. For this reason, an improved Faster RCNN was proposed to detect the defects in mugs. The Faster RCNN and feature pyramid network (FPN) were combined to increase the use of detailed shallow features, so as to achieve better detection effect for small targets. Faster RCNNs before and after improvement were used to conduct simulation on Caffe. The results show that Faster RCNN performs well in defect detection for mugs, but it misses some small targets. The improved Faster RCNN increases the detection accuracy by 2.485 percent at most for gaps and scratches and performs better in small target recognition.