Dongjie Li, Ruohao Li. Mug Defect Detection Method Based on Improved Faster RCNN[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041515
- Laser & Optoelectronics Progress
- Vol. 57, Issue 4, 041515 (2020)
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
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Table 1. ZF network structure before and after improvement
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Table 2. Comparison of various network structures on classification performance
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