Defect Recognition in Radioscopic Image Sequences based on Bag-of-Words. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Chang, Fan; Chen, Fumin; Tang, KaiXuan
• Is Part Of: Journal of physics. Volume 1820:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1820/1/012101
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Digital X-ray real-time imaging is a new technology in non-destructive detection using image sequences. At present, manual identification of defects is mainly used in practice, which is expertise-dependent and prone to errors. The existing automatic recognition method based on radiographic images mainly recognizes a single defect type for a single radiographic image, which is easy to produce a large number of false detections in non-defect areas, and cannot correctly identify images containing multiple types of defects. A defect recognition method based on Bag-of-Words is proposed, which uses the inter-frame difference method to obtain the moving target area, namely the defect area, and uses the SIFT operator based on the Bag-of-Words model to extract the defect features, and then the trained support vector machine (SVM) performs recognition. Experimental results show that the proposed method can effectively reduce false detections in non-defective areas. The defect recognition method correctly recognizes the defect category with a probability of 85.3%.