Research on Automatic Recognition of Casting Defects Based on Deep Learning

Abstract

A method for recognition of casting defects based on improved You Only Look Once (YOLO v3) is proposed to address the problems of slow detection speed, low detection efficiency, and poor robustness suffered from the current inspecting manually methods, which can improve the ability to detect defects, especially for tiny defects. Firstly, for obtained the industrial digital radiography images (DR images), we introduce the guide filtering technique to enhance the defects in these DR images, thus obtaining standard defect samples; Further, the defect samples are annotated to generate the defect detection data set for network training. In this article, the improved YOLOv3 network model structure is used to detect defects. Comparative experiments illustrate the proposed defect detection model for castings achieves better performance. Concretely, the experimental results show that the improved network model (YOLOv3_134) converges faster than the YOLOv3 network model and has better convergence than the YOLOv3 model. And the mean average precision (mAP) of the YOLOv3_134 is 26.1% higher than that of the original YOLOv3, which makes the YOLOv3_134 model-based casting defect detection method meet the industrial production requirements in terms of accuracy and speed.

Keywords

• Defect detection
• deep learning
• YOLOv3
• casting