## 1 Introduction
Considering the difficulties in detecting cracks in UAV remote sensing road images, we intend to construct a crack detection network based on two requirements. First, the network should be able to focus on the global features of cracks in the image and possess good noise immunity. Second, the network should be able to focus on more detailed information about cracks. To meet the first requirement, we design a multi-scale hybrid dilated convolution (MHDC) unit to be added to the bottom layer of the encoder to further increase the receptive field and obtain multi-scale contextual information. To meet the second requirement, feature fusion layers are added in each layer of the encoder, which can fuse the fusion results of all layers again at the end. We also design a multi-head self attention (MHSA) module to enhance the model's ability to acquire long-range structural information and obtain more detailed information about the cracks. In view of the above efforts, the CrackNet network can capture both the global features and the detailed information of cracks, achieving better crack detection performance for UAV remote sensing highway images. To address the issue that traditional crack classification algorithms cannot classify complex cracks in UAV remote sensing road images and cannot exclude environmental noise, we propose a classification algorithm called CrackClassification. This algorithm is based on gridding, depth-first traversal, and minimum enclosing frame and is used to classify complex cracks in crack detection maps. In addition, a series of preprocessingoperations, such as identifying the roadbed and obtaining the coordinates of the roadbed, are used to exclude environmental noise. The main contributions of our work can be summarized as follows. :  
1. In order to fill the gap of UAV remote sensing road crack dataset and facilitate subsequent research, we hand-labeled a UAV remote sensing road crack dataset, UAVRoadCrack. 2. This paper proposes a novel network for UAV remote sensing road crack detection, CrackNet. The use of the MHDC module, MHSA module, GN+ELU, and skip feature fusion layer in the CrackNet network enables efficient and accurate detection of highway cracks, saving significant human and material resources, and facilitating subsequent data analysis. CrackNet achieved the best average precision (AP) scores of 0.665, 0.942, and 0.895 on the UAVRoadCrack, CRKWH100, and CrackLS315 datasets, respectively. Based on other evaluation metrics, it also outperforms other mainstream crack detection algorithms. These results demonstrate that the CrackNet network is not only applicable to UAV remotely sensed road images but can also be used in other scenes captured at close range. 3. Our proposed preprocessing operation and CrackClassification algorithm can effectively exclude environmental noise and accurately classify complex cracks, which is essential for prioritizing maintenance and repair work. By calculating the confusion matrix of crack classification accuracy, we obtained precision rates of 0.839, 0.867, 0.740, and 0.778 for transverse cracks, longitudinal cracks, block cracks, and reticulated cracks, respectively. The recall for horizontal, vertical, block, and reticulated cracks is 0.868, 0.849, 0.738, and 0.772, respectively. This paper is organized as follows: Section 2 presents the related work. Section 3 presents the proposed methodology in both highway crack detection and highway crack classification. Section 4 describes the details and results of each part of the performed experiments. Section 5 summarizes the paper and details the outlook for future research work.