裂缝小目标缺陷的轻量化检测方法
Lightweight Detection Method for Small Crack Target Defects
贾晓芬 1江再亮 2赵佰亭2
作者信息
- 1. 安徽理工大学 电气与信息工程学院,安徽 淮南 232001;安徽理工大学 省部共建深部煤矿采动响应与灾害防控国家重点实验室,安徽 淮南 232001
- 2. 安徽理工大学 电气与信息工程学院,安徽 淮南 232001
- 折叠
摘要
及时且准确捕获井壁出现的微小裂缝,对于井筒安全意义重大.轻量化检测模型是推动井壁裂缝自动检测的关键,打破现有方法聚焦于提取深层语义信息的局限,重视浅层特征表征的几何结构信息的应用,针对井壁裂缝提出轻量化检测模型E-YOLOv5s.首先融合普通卷积、深度可分离卷积和ECA注意力机制设计轻量化卷积模块ECAConv,再引入跳跃链接构建特征综合提取单元E-C3,得到骨干网络ECSP-Darknet53,它负责显著降低网络参数,同时增强对裂缝深层特征的提取能力.然后设计特征融合模块ECACSP,利用多组ECAConv和ECACSP模块组建细颈部特征融合模块E-Neck,旨在充分融合裂缝小目标的几何信息和表征裂缝开裂程度的语义信息,同时加快网络推理速度.实验表明,E-YOLOv5s在自制井壁数据集上的检测精度相较YOLOv5s提升了4.0%,同时模型参数量和GFLOPs分别降低了44.9%、43.7%.E-YOLOv5s有助于推动井壁裂缝自动检测的应用.
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引用本文复制引用
基金项目
国家自然科学基金资助项目(52174141)
安徽省自然科学基金资助项目(2108085ME158)
安徽省高校协同创新项目(GXXT-2020-54)
安徽省重点研发计划支持项目(202004b11020029)
安徽省重点研发计划支持项目(202104a07020005)
出版年
2024
国家科技期刊平台
NSTL
万方数据