Study of x-ray image defect detection methods for girth welds

Computer based classification methodology is presented in the paper for defects being developed in the Al alloys as the car engine elements are made from them produced with the vacuum casting method. Identification of defects was carried out using data acquired from digital images obtained using the X-ray defect detection methods. The developed methodology as well as the related X-ray image analysis and quality control neural networks based software were carried out to solve this problem.

Download Full-text

Research on X-ray welding image defect detection based on convolution neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1237/3/032005 ◽

2019 ◽

Vol 1237 ◽

pp. 032005 ◽

Cited By ~ 1

Author(s):

Li Yaping ◽

Gao Weixin

Keyword(s):

Neural Network ◽

Defect Detection ◽

Convolution Neural Network ◽

X Ray ◽

Welding Image ◽

Image Defect

Download Full-text

A Lightweight Deep Network for Defect Detection of Insert Molding Based on X-ray Imaging

Sensors ◽

10.3390/s21165612 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5612

Author(s):

Benwu Wang ◽

Feng Huang

Keyword(s):

Defect Detection ◽

Molding Process ◽

X Ray ◽

Deep Network ◽

X Ray Imaging ◽

Common Causes ◽

Model Size ◽

The One ◽

Image Defect ◽

Insert Molding

Aiming at the abnormality detection of industrial insert molding processes, a lightweight but effective deep network is developed based on X-ray images in this study. The captured digital radiography (DR) images are firstly fast guide filtered, and then a multi-task detection dataset is constructed using an overlap slice in order to improve the detection of tiny targets. The proposed network is extended from the one-stage target detection method of yolov5 to be applicable to DR defect detection. We adopt the embedded Ghost module to replace the standard convolution to further lighten the model for industrial implementation, and use the transformer module for spatial multi-headed attentional feature extraction to perform improvement on the network for the DR image defect detection. The performance of the proposed method is evaluated by consistent experiments with peer networks, including the classical two-stage method and the newest yolo series. Our method achieves a mAP of 93.6%, which exceeds the second best by 3%, with robustness sufficient to cope with luminance variations and blurred noise, and is more lightweight. We further conducted ablation experiments based on the proposed method to validate the 32% model size reduction owing to the Ghost module and the detection performance enhancing effect of other key modules. Finally, the usability of the proposed method is discussed, including an analysis of the common causes of the missed shots and suggestions for modification. Our proposed method contributes a good reference solution for the inspection of the insert molding process.

Download Full-text

Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning

NDT & E International ◽

10.1016/j.ndteint.2019.102144 ◽

2019 ◽

Vol 107 ◽

pp. 102144 ◽

Cited By ~ 14

Author(s):

Wangzhe Du ◽

Hongyao Shen ◽

Jianzhong Fu ◽

Ge Zhang ◽

Quan He

Keyword(s):

Deep Learning ◽

Defect Detection ◽

X Ray ◽

Casting Aluminum ◽

Image Defect

Download Full-text

Deep Learning Based Defect Detection for Solder Joints on Industrial X-Ray Circuit Board Images

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) ◽

10.1109/indin45582.2020.9442142 ◽

2020 ◽

Author(s):

Qianru Zhang ◽

Meng Zhang ◽

Chinthaka Gamanayake ◽

Chau Yuen ◽

Zehao Geng ◽

...

Keyword(s):

Deep Learning ◽

Defect Detection ◽

Solder Joints ◽

Circuit Board ◽

X Ray

Download Full-text

Surface Defect Detection Methods Based on Deep Learning: a Brief Review

2020 2nd International Conference on Information Technology and Computer Application (ITCA) ◽

10.1109/itca52113.2020.00049 ◽

2020 ◽

Author(s):

Guanlin Liu

Keyword(s):

Deep Learning ◽

Defect Detection ◽

Surface Defect ◽

Detection Methods ◽

Surface Defect Detection

Download Full-text

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x21995388 ◽

2021 ◽

pp. 1748006X2199538

Author(s):

Zhenying Xu ◽

Ziqian Wu ◽

Wei Fan

Keyword(s):

Defect Detection ◽

Feature Fusion ◽

Recognition Rate ◽

Detection Methods ◽

Small Scale ◽

Detection Accuracy ◽

Single Shot ◽

Surface Defect Detection ◽

Feature Pyramid ◽

Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

Download Full-text