Faster RCNN based on Feature Fusion and its Application in Steel Surface Defect Detection
DOI:
https://doi.org/10.54691/sjt.v4i10.2400Keywords:
Surface Defect Detection; Faster RCNN; ResNet; Deep Learning.Abstract
Surface defects are an inevitable problem in steel production and processing. To solve the problems of error detection, leakage detection and low accuracy of traditional manual methods for steel surface defect detection, this paper proposes a steel surface defect detection algorithm based on Faster RCNN, a classical model in the field of computer vision object detection. The proposed model uses ResNet50 as its backbone and introduces FPN to fuse the multi-layer feature maps of the backbone to improve the detection capability for defects of different scales. The experimental results on NEU-DET dataset show that the proposed model in this paper has different detection accuracies for six different types of defects in the dataset, and the overall mAP reaches 0.708. In addition, the model execution speed reaches 43.3 frame/s, which meets the applications in industrial scenarios.
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