信阳师范学院学报（自然科学版）2024，Vol.37Issue(4)：470-476,7.DOI:10.3969/j.issn.1003-0972.2024.04.008
基于通道和空间注意力的带钢表面缺陷显著性目标检测
Channel and Spatial Attention Based Salient Object Detection of Surface Defects in Strip Steel
摘要
Abstract
Steel surface defect detection is becoming increasingly important in industrial product quality control.Due to the complex background,variety of defects and different scales of steel surface defects,accurate and efficient detection of strip surface defects is still a challenging task.In order to solve these problems,a strip surface defect salient object detection model based on channel and spatial attention was proposed.Firstly,based on Transformer,the multiscale features of strip steel images were extracted to capture the long-distance dependence of the target image;Then,the acquired feature map was fed into two different attention modules:The channel attention module and the spatial attention module,in order to emphasize the characteristics of the strip steel surface defect and suppress irrelevant background features,so as to strengthen the ability to distinguish between the defect target and the background;Finally,the multiscale progressive fusion module was used to integrate the multiscale feature map,so that the feature information of different scales could be complementary,obtaining feature maps with rich semantic information which enables the model to detect strip surface defects highly efficient and accurately.A large number of experimental results showed that the proposed model had great advantages in the salient object detection task with higher accuracy and stronger robustness.关键词
带钢/缺陷检测/空间注意力/通道注意力/特征融合Key words
strip steel/defect detection/spatial attention/channel attention/feature fusion分类
信息技术与安全科学引用本文复制引用
郭华平,李锡瑞,张莉,孙艳歌,付志鹏..基于通道和空间注意力的带钢表面缺陷显著性目标检测[J].信阳师范学院学报（自然科学版）,2024,37(4):470-476,7.基金项目
国家自然科学基金项目(62002307) （62002307）
河南省自然科学基金项目(222300420275) （222300420275）
河南省科技计划项目(242102210092) （242102210092）
河南省研究生教育优质课程项目(YJS2022KC34) （YJS2022KC34）
信阳师范大学研究生科研创新基金项目(2024KYJJ010) （2024KYJJ010）
