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

Wangzhe Du, Hongyao Shen (), Jianzhong Fu, Ge Zhang, Xuanke Shi and Quan He
Additional contact information
Wangzhe Du: College of Mechanical Engineering, Zhejiang University
Hongyao Shen: College of Mechanical Engineering, Zhejiang University
Jianzhong Fu: College of Mechanical Engineering, Zhejiang University
Ge Zhang: College of Mechanical Engineering, Zhejiang University
Xuanke Shi: College of Mechanical Engineering, Zhejiang University
Quan He: College of Mechanical Engineering, Zhejiang University

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 1, No 10, 156 pages

Abstract: Abstract 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.

Keywords: Defect detection; Casting parts; Deep learning; X-ray image; Computer vision (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-020-01566-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:32:y:2021:i:1:d:10.1007_s10845-020-01566-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-020-01566-1

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().