计算机工程2025，Vol.51Issue(5)：351-360,10.DOI:10.19678/j.issn.1000-3428.0068622
基于多维注意力模块的轻量化混凝土裂缝检测方法
Lightweight Concrete Crack Detection Method Based on Multi-Dimensional Attention Module
摘要
Abstract
Most current concrete crack detection models are too large to be deployed in mobile devices,and the crack detection is inaccurate,with cracks being missed;to solve these problems,a lightweight concrete crack detection method based on a multi-dimensional attention module is proposed.In this method,because most current mainstream crack detection models are large,depth-separable convolution is used to reconstruct the Conv-BN-SiLU(CBS)feature extraction module in YOLOv5s to obtain a Lightweight(LCBS)module.To solve the problem of inaccurate crack detection,a Multi-Scale Feature(MSF)module is proposed to replace the convolution layer of the first layer of YOLOv5s to enhance the ability of the network to extract the features of cracks of different sizes.To address the problem of missed cracks,a Multi-Dimensional Attention(MDA)module,which fuses spatial and channel information,is proposed to enhance the ability of crack feature extraction and reduce the number of missed cracks.Experiments show that,compared with YOLOv5s,the proposed method reduces the number of parameters by 35.2％,computation amount by 50.9％,and model size by 32.8％and increases the average accuracy(mAP@0.5)by 4.2 percentage points.Compared with other mainstream target detection methods of the same type,the proposed method has fewer parameters and higher detection accuracy.关键词
裂缝检测/注意力模块/轻量化/YOLOv5s模型/目标检测Key words
crack detection/attention module/lightweight/YOLOv5s model/object detection分类
信息技术与安全科学引用本文复制引用
许华杰,郑力文,张品,秦远卓..基于多维注意力模块的轻量化混凝土裂缝检测方法[J].计算机工程,2025,51(5):351-360,10.基金项目
广西自然科学基金(2024JJA170106) （2024JJA170106）
广西重点研发计划项目(桂科AD25069071) （桂科AD25069071）
国家自然科学基金(52169021). （52169021）
