###### Abstract
Therefore, in order to ensure the efficiency, discrimination, and generalization of visual feature learning, it is necessary to study the design and improvement of algorithm frameworks for different scenarios and challenges from the three aspects of the loss function, sample mining, and network model, so as to meet the actual application requirements. Research on the key technologies of visual feature learning aims to explore and mine the problems and challenges existing in deep learning models and improve and optimize them, so as to obtain model sub-modules (e.g., sample mining, objective function, and network structure) that can ensure the discrimination, efficiency, and generalization of feature learning. In recent decades, benefiting from the development of artificial intelligence technology, various research efforts have been devoted to establishing road crack classification models. Researchers are attempting to apply traditional machine learning or deep learning algorithms to object crack detection [9; 10; 11]. These artificial-intelligence-based recognition methods make precise identification of cracks possible. The key to artificial intelligence is to learn features and apply the learned features to new data. Traditional machine learning algorithms such as logistic regression [12], random forest [13], artificial neural networks [14], support vector machine (SVM), etc., have been widely used in crack detection. Artificial neural network models with appropriate thresholds have been used to separate crack pixels from the background [15]. In order to overcome the shortcomings of traditional machine learning that require prior knowledge to extract target features, deep learning techniques represented by deep convolutional neural networks [16] have been widely applied in fields such as image classification, object detection, and image segmentation and have shown potential to compete with humans [17]. Unlike traditional machine learning, deep learning models automatically learn the relationship between features and tasks. In recent years, many high-performance deep convolutional neural network models based on the large universal raw dataset ImageNet [18], such as AlexNet [19], VGGNet [20], ResNet [21], and DenseNet [22], have been developed, and their classification performance is sufficient to match humans [23]. Benefiting from these deep convolutional neural network models,many crack classification algorithms [24; 25; 26; 27; 28] based on deep convolutional neural networks have been proposed and successfully applied in various fields [29; 30; 31; 32; 33; 34; 35; 36]. Visual feature learning based on deep learning is considered to be a challenging task in the era of artificial intelligence, especially when deep learning is widely used and prevalent. Rapid and accurate expression of features can be widely and successfully used in social practice, which is an important research problem at present, for example, video analysis and face recognition in the field of intelligent security, scene recognition and road crack detection in the field of traffic, product search and matching recommendation in the field of daily life, image positioning and scene recognition in the field of unmanned driving, etc. The above fields require high discrimination, efficiency, and generalization of visual feature learning, which is related to people's quality of life and social security. However, current research faces different problems in different scenarios, which is far from meeting the actual needs. Therefore, the research on the key technologies of visual feature learning based on deep learning has practical needs and social significance. Fundamentally, the ultimate goal of deep learning is to build a machine learning architecture that consists of multiple processing layers that learn multiple levels of abstraction to represent data. The use of this method greatly improves the development level of speech recognition, visual target recognition, target detection, and other subjects.