湖南大学学报（自然科学版）2024，Vol.51Issue(6)：52-62,11.DOI:10.16339/j.cnki.hdxbzkb.2024266
裂缝小目标缺陷的轻量化检测方法
Lightweight Detection Method for Small Crack Target Defects
摘要
Abstract
Timely and accurately capturing the tiny cracks in the shaft lining is of great significance for shaft safety.Lightweight detection models are the key to realizing the automatic detection of shaft lining cracks.Departing from existing traditional methods that focus on extracting deep semantic information,the application of geometric structure information represented by shallow features should be paid attention to and a lightweight detection model E-YOLOv5s for shaft lining cracks is proposed.Firstly,the lightweight convolution module,ECAConv,is designed,which integrates traditional convolution,depth-separable convolution,and an attention mechanism called ECA.Then,thefeature extraction capabilities are further enhanced by incorporating skip connections to construct the feature comprehensive extraction unit,E-C3.Thereby,the backbone network ECSP-Darknet53 is obtained,which significantly reduces network parameters and enhances the ability to extract deep fracture features of cracks.Finally,the feature fusion module ECACSP is proposed and the thin neck feature fusion module E-Neck is built by using multiple groups of ECAConv and ECACSP modules.The purpose of E-Neck is to fully fuse the geometric information of small crack targets and the semantic information of crack cracking degrees while accelerating the network reasoning.Experimental results show that the detection accuracy of E-YOLOv5s on the self-made shaft lining dataset is improved by 3.3%compared to YOLOv5s while the number of model parameters and GFLOPs are reduced by 44.9%and 43.7%,respectively.E-YOLOv5s can help promote the application of automatic detection of shaft lining cracks.关键词
裂缝缺陷/小目标/深度学习/深度可分离卷积Key words
crack defects/small targets/deep learning/depth-separable convolution分类
信息技术与安全科学引用本文复制引用
贾晓芬,江再亮,赵佰亭..裂缝小目标缺陷的轻量化检测方法[J].湖南大学学报（自然科学版）,2024,51(6):52-62,11.基金项目
国家自然科学基金资助项目(52174141),National Natural Science Foundation of China(52174141) （52174141）
安徽省自然科学基金资助项目(2108085ME158),Natural Science Foundation of Anhui Province(2108085ME158) （2108085ME158）
安徽省高校协同创新项目(GXXT-2020-54),Collaborative Innovation Project in Anhui Universities(GXXT-2020-54) （GXXT-2020-54）
安徽省重点研发计划支持项目(202004b11020029,202104a07020005),Key Research and Development Program Supported Projects in Anhui Province(202004b11020029,202104a07020005) （202004b11020029,202104a07020005）
