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应用科学学报 >

2023 , Vol. 41 >Issue 6: 978 - 988

DOI: https://doi.org/10.3969/j.issn.0255-8297.2023.06.006

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基于边缘感知深度残差网络的带钢表面缺陷检测

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  • 杭州电子科技大学 自动化学院, 浙江 杭州 310018

收稿日期: 2021-12-07

  网络出版日期: 2023-11-30

基金资助

国家自然科学基金（No.62271180）；国家重点研发项目（No.2020YFB1406604）；浙江省自然科学基金（No.LY19F030022）；杭电-中电大数据技术工程研究中心基金（No.KYH063120009）资助

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

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  • School of Automation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China

Received date: 2021-12-07

  Online published: 2023-11-30

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摘要

基于深度学习的显著目标检测方法已被用于带钢表面缺陷检测中，但仍存在模型收敛速度慢、检测结果边缘不清晰等问题。针对现有问题，本文提出了基于边缘感知深度残差网络（boundary-aware deeply residual network,BADRNet），以此进行带钢表面缺陷的显著目标检测。将边缘信息引入至缺陷检测任务中，解决了因目标尺寸多样性带来的检测结果边缘不清晰的问题；通过在边缘提取、显著特征融合部分采用具有残差结构的3个卷积层作为基本块，提高了训练效率且保持原有的检测精度不变。在公开的SD-saliency-900数据集上的实验结果表明，所提模型相比于现有模型，在6个评价指标上均取得了最优效果。BADRNet比当前最优的EDRNet在S-measure指标上提升了1.6%，同时对于缺陷区域边缘的检测效果具有明显提升。

关键词： 显著性检测; 缺陷检测; 深度学习; 残差结构; 边缘特征

本文引用格式

沈坤烨, 周晓飞, 费晓波, 陈雨中, 张继勇, 颜成钢 . 基于边缘感知深度残差网络的带钢表面缺陷检测[J]. 应用科学学报, 2023 , 41(6) : 978 -988 . DOI: 10.3969/j.issn.0255-8297.2023.06.006

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

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