应用科学学报 ›› 2023, Vol. 41 ›› Issue (6): 978-988.doi: 10.3969/j.issn.0255-8297.2023.06.006
沈坤烨, 周晓飞, 费晓波, 陈雨中, 张继勇, 颜成钢
收稿日期:
2021-12-07
出版日期:
2023-11-30
发布日期:
2023-11-30
通信作者:
周晓飞,副教授,研究方向为视频与图像处理、深度学习。E-mail:zxforchid@outlook.com
E-mail:zxforchid@outlook.com
基金资助:
SHEN Kunye, ZHOU Xiaofei, FEI Xiaobo, CHEN Yuzhong, ZHANG Jiyong, YAN Chenggang
Received:
2021-12-07
Online:
2023-11-30
Published:
2023-11-30
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。
中图分类号:
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.
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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