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

doi:10.12068/j.issn.1005-3026.2021.05.012

Abstract

Abstract: 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.

Key words: hot-rolled plate surface; defect detection; weakly-supervised learning; multi-level feature integration; class activation mapping

CLC Number:

TP391.4

HE Yu， SONG Ke-chen， ZHANG De-fu， YAN Yun-hui. Weakly-Supervised Steel Plate Surface Defect Detection Algorithm by Integrating Multiple Level Features[J]. Journal of Northeastern University(Natural Science), 2021, 42(5): 687-692.

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