Weakly-Supervised Steel Plate Surface Defect Detection Algorithm by Integrating Multiple Level Features

被引：1
|
作者：
He Y. [1 ]
Song K.-C. [1 ]
Zhang D.-F. [1 ]
Yan Y.-H. [1 ]
机构：
[1] School of Mechanical Engineering & Automation, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2021年 / 42卷 / 05期
关键词：
Class activation mapping; Defect detection; Hot-rolled plate surface; Multi-level feature integration; Weakly-supervised learning;
D O I：
10.12068/j.issn.1005-3026.2021.05.012
中图分类号：
学科分类号：
摘要：
Due to the lack of instance-level annotations, the application of deep neural network in the field of industrial surface detection is limited. In order to solve this problem, a weakly-supervised-learning-based defect detection network is proposed for the practical defect detection task of hot rolled steel plate surfaces. This network introduces the class activation mapping model, which can be used to train the model with the image-level annotations that can be obtained relatively easily, and performs the defect detection on the surface of steel plates. In order to further improve the detection accuracy and overcome the shortcomings of the class activation mapping model, the residual network with better performances is used as the backbone network for feature extraction, and the multi-level feature integration network is proposed to generate the class activation maps. In this way, the network can obtain more detail information and activate target areas more accurately. Extensive experiments have been carried out on the NEU-CLS dataset, and the results show that the proposed method can detect defects with incomplete labels, and obtain the classification error rate of 0.68% and the localization error rate of 17.75%, which are better than the other related methods. © 2021, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：687 / 692
页数：5
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