Defect Detection of Metal Surface Based on Attention Cascade R-CNN

Abstract

Automatic metal surface defect detection is an important part of quality control in industrial production. In complex industrial scenarios, traditional image processing methods cannot detect defect areas effectively, and manual inspection is time-consuming and labor-intensive. How to quickly and effectively detect defects for metal surface has become the key to improve the efficiency of the production. However, the complex lighting conditions on the metal surface are prone to strong reflections and reflections, and defects are varied and have unclear boundaries, which poses a great challenge to defect detection. This paper proposes a novel cascade R-CNN (region-based convolutional neural network) defect detection method based on attention mechanism to classify and locate metal surface defects with high-quality. A lightweight network module is designed to calculate attention along two separate dimensions, spatial and channel. It can be inserted into a convolutional neural network and effectively improve the feature extraction ability. To improve the detection accuracy, two cascade detection heads are trained with increasing IoU thresholds. The output of the previous head is used as the next training set for the next head to refine the detection results in turn. In addition, various factors affecting performance are explored in a large number of experi-ments. Compared with existing methods, the proposed method has high accuracy and good robustness, and can be practically applied in production.

Keywords

• defect detection; object detection
• attention mechanism
• deep learning
• convolutional neural network (cnn)