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 information 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 network 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%.

Key words: crack detection, attention mechanism, feature pyramid, multi-scale feature, Transformer

摘要： 现有的道路裂缝检测方法通常依赖于局部特征进行检测，导致目标的结构信息和上下文关联性不足，从而影响检测精度。为解决这一问题，提出一种基于深度特征提取与多尺度信息融合的道路裂缝检测方法Crack-YOLOv7。在骨干网络中引入PSA（pyramid split attention）模块，以增强特征图的上下文信息和位置感知能力，获取更加丰富的特征信息；同时提出了SSPPF（spatial stage pyramid pooling fast）模块，提升了网络的推理速度并有效增强了前馈信息的传递；提出了S2DT-FPN（spatial-shift dilated transformer feature pyramid network）结构，通过多尺度特征融合与跨层依赖的建立，进一步捕捉了不同语义深度的特征信息，同时保留了全局上下文特征；由于道路裂缝形态的多样与重叠问题，采用柔性非极大抑制（Soft-NMS）算法，改善了密集裂缝场景下的检测精度。在RDD2020数据集上的实验结果表明，所提方法能够有效地从损伤图像中实现路面裂缝的检测，检测精度达到89.7%，且mAP（mean average precision）值达到65.5%。

关键词: 裂缝检测, 注意力机制, 特征金字塔, 多尺度特征, Transformer