重庆理工大学学报2025，Vol.39Issue(1)：93-101,9.DOI:10.3969/j.issn.1674-8425(z).2025.01.012
优化纹理匹配的路面裂缝检测模型
Optimizing texture matching for pavement crack detection models
摘要
Abstract
In recent years,road maintenance has gained keen academic interest.The detection of pavement damage is a key part in road maintenance.Existing deep learning crack detection has problems such as information loss and positional offset in the process of feature extraction.Thus,misdetection and omission of cracks often occur when noise interference such as complex texture pavement background and shadows exists.We propose a crack texture matching method based on reference-based super-resolution idea.The optimized global context block(GC block)enhances the spatial dependence between pixels in the feature image and borrows high-resolution spatial texture from the reference image of the upper coding layer to compensate the information loss in the low-resolution feature image.Then,it builds the Texture Matching Network(TMNet)for the detection of cracks in pavement.The prediction model is validated on the public datasets(Crack500,DeepCrack)and our independently built dataset.Our results show the TMNet reaches 78.35%and 89.86%in MIoU on the two public datasets and 77.68%on our own dataset,demonstrating it performs better in detail texture recovery compared to other networks.关键词
路面裂缝检测/语义分割/参考的超分辨率/注意力机制/纹理匹配Key words
pavement crack detection/semantic segmentation/super-resolution of reference/attention mechanism/texture matching分类
交通工程引用本文复制引用
沈德争,于滨..优化纹理匹配的路面裂缝检测模型[J].重庆理工大学学报,2025,39(1):93-101,9.基金项目
国家电网有限公司科技项目(J2023055) （J2023055）
