Automated detection of defects with low semantic information in X-ray images based on deep learning

Nondestructive testing using X-ray imaging has been widely adopted in the defect detection of casting parts for quality management. Deep learning has been proved to be an effective way to detect defects in X-ray images. In this work, Feature Pyramid Network (FPN) which has been utilized broadly in many applications is adopted as our baseline. In FPN, there mainly exits two issues: firstly, down sampling operation in Convolutional Neural Network is often utilized to enhance the perception field, causing the loss of location information in feature maps, and secondly, there exists feature imbalance in feature maps and proposals. DetNet and Path Aggregation Network are adopted to solve the two shortages. To further improve the recall rate, soft Non-Maximum Suppression (soft-NMS) is adopted to remain more proposals that have high classification confidence. Defects in X-ray images of casting parts are provided with low semantic information, causing the different instances between detection results and annotations in the same area. We propose soft Intersection Over Union (soft-IOU) criterion which could evaluate several results or ground truths in the near area, making it more accurate to evaluate detection results. The experimental results demonstrate that the three proposed strategies have better performance than the baseline for our dataset.

Automated summary

In this study, X-ray imaging was used for nondestructive testing of casting parts to detect defects, and deep learning techniques such as Feature Pyramid Network, DetNet, Path Aggregation Network, and soft Non-Maximum Suppression were adopted to improve the recall rate and evaluation accuracy. Experimental results showed better performance compared to the baseline.