基于边缘感知深度残差网络的带钢表面缺陷检测

doi:10.3969/j.issn.0255-8297.2023.06.006

Abstract

Abstract: Deep learning-based salient object detection has been used in strip steel surface defects, but there are still some problems such as slow model training speed and unclear boundary of detection results. To address these issues, we proposed a boundary-aware deeply residual network (BADRNet) for salient object detection of strip steel surface defects. Boundary features are introduced into the steel surface defects to solve the problem of unclear boundary of detection results caused by varying object sizes. Three convolution layers with residual structure are used as basic blocks for boundary extraction and salient feature aggregation, improving training efficiency while maintaining original detection accuracy. Experimental results on the public strip steel benchmark dataset, SD-saliency-900, show that our model outperforms existing models in all six evaluation indicators. The proposed BADRNet improves the S-measure performance by 1.6%, and significantly enhances the detection effect on the defect area.

Key words: salient object detection, defect detection, deep learning, residual structure, boundary feature

CLC Number:

TP391

SHEN Kunye, ZHOU Xiaofei, FEI Xiaobo, CHEN Yuzhong, ZHANG Jiyong, YAN Chenggang. Boundary-Aware Deeply Residual Network for Salient Object Detection of Strip Steel Surface Defects[J]. Journal of Applied Sciences, 2023, 41(6): 978-988.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2023.06.006

https://www.jas.shu.edu.cn/EN/Y2023/V41/I6/978

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Boundary-Aware Deeply Residual Network for Salient Object Detection of Strip Steel Surface Defects

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