计算机工程与应用2025，Vol.61Issue(18)：198-208,11.DOI:10.3778/j.issn.1002-8331.2412-0120
Crack-YOLOv7:深度特征提取与多尺度信息融合的道路裂缝检测
Crack-YOLOv7:Road Crack Detection Based on Deep Feature Extraction and Multi-Scale Information Fusion
摘要
Abstract
The existing road crack detection methods usually rely on local features for detection,resulting in insufficient structural information and context relevance of the target,thus affecting the detection accuracy.In order to solve this problem,a pavement crack detection method Crack-YOLOv7 based on depth feature extraction and multi-scale informa-tion fusion is proposed.Firstly,the PSA(pyramid split attention)module is introduced into the backbone network to enhance the context information and location awareness of the feature map and obtain richer feature information.At the same time,the SSPPF(spatial stage pyramid pooling fast)module is designed to improve the inference speed of the net-work and effectively enhance the transmission of feedforward information.Secondly,the S2DT-FPN(spatial-shift dilated transformer feature pyramid network)structure is proposed.Through multi-scale feature fusion and cross-layer dependency establishment,the feature information of different semantic depths is further captured,while the global context features are retained.Finally,due to the diversity and overlap of road crack morphology,the flexible non-maximal suppression(Soft-NMS)algorithm is used to improve the detection accuracy in dense crack scenarios.The experimental results on the RDD2020 dataset show that the proposed method can effectively detect pavement cracks from the damaged image.The detection accuracy reaches 89.7%,and the mean average precision(mAP)value reaches 65.5%.关键词
裂缝检测/注意力机制/特征金字塔/多尺度特征/TransformerKey words
crack detection/attention mechanism/feature pyramid/multi-scale feature/Transformer分类
信息技术与安全科学引用本文复制引用
张咏琪,王杰,邓彬,周渝皓,杨珺旎..Crack-YOLOv7:深度特征提取与多尺度信息融合的道路裂缝检测[J].计算机工程与应用,2025,61(18):198-208,11.基金项目
国家重点研发计划项目(2022YFB4703600). （2022YFB4703600）
