关键词: 裂缝检测, 扩张卷积, 有效感受野, 注意力机制, 特征融合

Abstract:

Pavement crack detection is a critical technology used to judge the safety of roads. Due to the complex and diverse background of cracks, it is difficult for traditional crack detection algorithms to accurately detect cracks. This paper proposes an automatic crack detection algorithm that enhances the fusion of semantic information and multi-channel features. The entire network is an encoder-decoder structure. A dilated convolution module is introduced in the encoder part to expand the effective receptive field of the feature map, integrate the image context information, enhance the semantic expression ability of features, and improve the accuracy of pixel classification. In the decoder part, a multi-channel feature fusion module based on attention mechanism is built, which uses high-level global attention information to guide the step-by-step fusion of high-level semantic features and low-level detail features. It is helpful to restore the detailed information of the image and further improve the pixel-level detection accuracy of cracks. The experiment results show that the model trained on the CRACK500 public data set achieves an average Intersection over Union（IoU） of 72.5% and an F1score of 96.8% on the test set. This model is directly used on the CrackForest data set for test, and the average IoU and F1score are improved by 2.0 percentage points and 1.1 percentage points respectively, indicating that the model has good generalization performance and can be used for crack detection and quality evaluation in complex road scenarios.

Key words: crack detection, dilated convolution, effective receptive field, attention mechanism, feature fusion