Fig. 1. FPN+RPN algorithm network structure diagram

Fig. 2. Partial training samples. (a) With four defects, one gap, one scratch, and two speckles; (b) with one speckle defect; (c) with two gap defects

Fig. 3. Partial test samples. (a) With one gap defect; (b) with two defects, one gap and one speckle; (c) with one scratch defect

Fig. 4. Training loss based on ZF network. (a) Stage-1 training loss of RPN; (b) stage-1 training loss of Faster RCNN; (c) stage-2 training loss of RPN; (d) stage-2 training loss of Faster RCNN

Fig. 5. Training loss based on improved ZF network. (a) Stage-1 training loss of RPN; (b) stage-1 training loss of Faster RCNN; (c) stage-2 training loss of RPN; (d) stage-2 training loss of Faster RCNN

Fig. 6. Labeling data set with labelImg

Fig. 7. Comparison of mug defect inspection results. (a) Original Faster RCNN; (b) Faster RCNN after FPN addition