###### Abstract  
Road crack detection is an important indicator of road detection. In real life, it is very meaningful work to detect road cracks. With the rapid development of science and technology, especially computer science and technology, quite a lot of methods have been applied to crack detection. Traditional detection methods rely on manual identification, which is inefficient and prone to errors. In addition, the commonly used image processing methods are affected by many factors, such as illumination, road stains, etc., so the results are unstable. In the research on pavement crack detection, many research studies mainly focus on the recognition and classification of cracks, lacking the analysis of the specific characteristics of cracks, and the feature values of cracks cannot be measured. Starting from the deep learning method in computer science and technology, this paper proposes a road crack detection technology based on deep learning. It relies on a new deep dictionary learning and encoding network DDLCN, establishes a new activation function MeLU, and adopts a new differentiable calculation method. The technology relies on the traditional Mask-RCNN algorithm and is implemented after improvement. In the comparison of evaluation indicators, the values of recall, precision, and F1-score reflect certain superiority. Experiments show that the proposed method has good implementability and performance in road crack detection and crack feature measurement. c 202313122991322122991322131222913122222222222222222  
crack probability map is established by using tensor voting, which enhances the connection between cracks and makes the cracks have good continuity. [PERSON] et al. [4] proposed a noise reduction and enhancement method for cement pavement. According to the noise spectrum characteristics of the picture, the relative position of the noise in the picture is inferred and filtered by the filter, which can effectively remove the noise interference of cement pavement. [PERSON] [5] proposed an automatic detection method of pavement cracks in digital images based on wavelet transform. This method uses the pavement crack image enhancement algorithm to correct the uneven background illumination by calculating the multiplicative factor that eliminates the background illumination change. This method can effectively extract pavement cracks in a variety of pavement images. The method discussed in this paper is a method of crack feature extraction and analysis based on deep learning. Visual feature learning based on deep learning is a core research problem in today's computer vision tasks. Its key is to quickly learn visual feature expressions with high discrimination and generalization through deep learning network models, which is the result of the joint action of sample mining, the network model, and the supervised optimization of the loss function. Among them, the loss function plays a supervisory role in the model, which realizes the training and optimization of the network model by punishing the error samples. In the current research on computer vision tasks, the algorithmic models based on deep learning widely use deep metric learning loss functions (usually applied to vision tasks such as image retrieval [6], image classification [7], and object recognition [8]) and class-based probability loss functions (usually applied to vision tasks such as object detection and image segmentation). They impact the discrimination ability and efficiency of feature learning, in different scenarios and tasks, with different challenges. The performance of the loss function is affected by sample mining. Excellent sample mining methods can quickly and automatically select informative samples from a large number of complex samples. In addition, the deep network model also affects the performance of visual feature learning.