微型电脑应用2026，Vol.42Issue(4)：34-37,4.
应用改进YOLOX轻量化卷积神经网络的水下工程裂纹识别
Application of Improved YOLOX Lightweight Convolutional Neural Network for Underwater Engineering Crack Recognition
摘要
Abstract
Aiming at the drawbacks of excessive parameters and low efficiency in the convolutional neural network model,an underwater engineering crack recognition method based on improved YOLOX lightweight convolutional neural network is pro-posed.The depthwise separable convolution is used to replace ordinary convolution in YOLOX network,to reduce model re-dundant parameters and achieve lightweight design.On the basis of the YOLOX backbone feature extraction network frame-work,a Transformer visual module is embedded to replace the cross stage partial(CSP)loop structure at the end of the back-bone network.The attention mechanism is added to enhance the ability to extract key information.The experimental results show that the proposed method has good predictive ability,and the crack recognition error is generally between 1.10％and 1.40％.The ablation experiment shows that the underwater crack recognition accuracy of the proposed method reaches 96.5％,with a frame rate of 31.2,presenting the optimal recognition accuracy while ensuring a high crack recognition rate.关键词
裂纹识别/轻量化网络/卷积神经网络/YOLOXKey words
crack recognition/lightweight network/convolutional neural network/YOLOX分类
建筑与水利引用本文复制引用
戴丽媛,杨丽雅,田晓丹,沈长松..应用改进YOLOX轻量化卷积神经网络的水下工程裂纹识别[J].微型电脑应用,2026,42(4):34-37,4.基金项目
安徽省教育厅2024年质量工程"四新"研究与改革实践项目(2024sx212) （2024sx212）
安徽省教育厅2023年高校科学研究重点项目(2023AH052490) （2023AH052490）
马鞍山市水工程健康诊断与修复技术研究中心2023年度开放基金项目(2023msgc002) （2023msgc002）
马鞍山市丘陵地区水资源高效利用工程技术研究中心2023年度开放基金项目(WREU202302) （WREU202302）
