Research on Automatic Recognition of Casting Defects Based on Deep Learning

Liming Duan,Lang Ruan,Ke Yang

doi:10.1109/access.2020.3048432

Liming Duan, Lang Ruan + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3048432

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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.

Highlights

Casting defects, such as blowholes, cracks, porosities and slag inclusions, affect the quality of the parts of the machine, and reduce the work performance of the machine, which could cause serious traffic accidents and National property damage
We propose a deep learning-based method for the defect of castings detection in DR images, which can effectively avoid the occurrence of human mis-inspection and missed inspection and improve the accuracy of casting defect detection
Due to the small size and different shapes of the defect in the DR images of the casting, we propose a method that incorporates dual-density convolutional layers into YOLOv3 to reduce the rate of missed flaw and improve the accuracy of the network

Summary

Introduction

Casting defects, such as blowholes, cracks, porosities and slag inclusions, affect the quality of the parts of the machine, and reduce the work performance of the machine, which could cause serious traffic accidents and National property damage. We propose a deep learning-based method for the defect of castings detection in DR images, which can effectively avoid the occurrence of human mis-inspection and missed inspection and improve the accuracy of casting defect detection.

Results

Conclusion