Abstract
Deep learning networks are applied for defect detection, among which Cascade R-CNN is a multi-stage object detection network and is state of the art in terms of accuracy and efficiency. However, it is still a challenge for Cascade R-CNN to deal with complex and diverse defects, as the widely varied shapes of defects lead to inefficiency for the traditional convolution filter to extract features. Additionally, the imbalance in features, losses and samples cause lower accuracy. To address the above challenges, this paper proposes a multi-stage balanced R-CNN (MSB R-CNN) for defect detection based on Cascade R-CNN. Firstly, deformable convolution is adopted in different stages of the backbone network to improve its adaptability to the varying shapes of the defect. Then, the features obtained by the backbone network are refined and enhanced by the balanced feature pyramid. To overcome the imbalance of classification and regression loss, the balanced L1 loss is applied at different stages to correct it. Finally, for the sample selection, the interaction of union (IoU) balanced sampler and the online hard example mining (OHEM) sampler are combined at different stages to make the sampling more reasonable, which can bring a better accuracy and convergence effect to the model. The results of our experiments on the DAGM2007 dataset has shown that our network (MSB R-CNN) can achieve a mean average precision (mAP) of 67.5%, an increase of 1.5% mAP, compared to Cascade R-CNN.
Highlights
Defect detection [1], an important task in computer vision, has attracted widespread attention in recent years
Cai et al [7] proposed Cascade R-CNN to address the interaction of union (IoU) imbalance, and achieved state-of-the-art performance in object detection tasks [8] by designing a cascaded network structure and a gradually increased IoU threshold at each stage
The performance is very limited when Cascade R-CNN is directly applied in a defect detection task
Summary
Defect detection [1], an important task in computer vision, has attracted widespread attention in recent years. Defect detection faces challenges of imbalances in different levels of features and multiple loss functions in an object detection network. A balanced network often encourages a better performance of computer vision tasks. Cai et al [7] proposed Cascade R-CNN to address the interaction of union (IoU) imbalance, and achieved state-of-the-art performance in object detection tasks [8] by designing a cascaded network structure and a gradually increased IoU threshold at each stage. The performance is very limited when Cascade R-CNN is directly applied in a defect detection task. Compared with Grid R-CNN [9], Cascade R-CNN achieved lower accuracy (66.0% mAP) than Grid R-CNN (66.5% mAP) on the DAGM2007 [10] dataset but with more parameters and computational costs
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