摘要
针对山区地表裂缝检测存在背景复杂、裂缝和背景像素比例不均衡现象,以及传统裂缝检测算法的效果差,精度低、泛化性能不足等问题,提出采用DeepCrack网络作为基本构架,同时对基础网络进行改进,在网络中加入位置注意力机制,调整损失函数,并将优化器换为Adan优化器。为证明所提出算法的有效性和准确性,将DeepCrack数据集与人工标注的数据集结合,采用F-feature指标来评估检测性能。实验结果表明,改进算法在数据集相对原算法整体精度提升1.61%,因此改进算法提升了裂缝检测的准确性,具有较好的检测效果。
Aiming at the problems of complex background,unbalanced ratio of cracks and background pixels in mountainous ar⁃eas,as well as the poor effect of traditional crack detection algorithms,low accuracy,and insufficient generalisation performance,the DeepCrack network is proposed as the basic architecture,while the basic network is improved by adding a Coordinate Attention mecha⁃nism,adjusting the loss function,and replacing the optimiser with the Adan optimiser.To demonstrate the effectiveness and accuracy of the proposed algorithm,the DeepCrack dataset is combined with the manually labelled dataset,and the F-feature metric is used to eval⁃uate the detection performance.The experimental results show that the overall accuracy of the improved algorithm in the dataset is im⁃proved by 1.61%relative to the original algorithm,so the improved algorithm enhances the accuracy of crack detection and has better detection results.
作者
黄海新
郭鹏
HUANG Haixin;GUO Peng(School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang,110159,China)
出处
《通信与信息技术》
2024年第3期87-91,102,共6页
Communication & Information Technology
