# Detection Based on Crack Key Point and Deep Convolutional Neural Network
###### Abstract  
Based on the features of cracks, this research proposes the concept of a crack key point as a method for crack characterization and establishes a model of image crack detection based on the reference anchor points method, named KP-CraNet. Based on ResNet, the last three feature layers are repurposed for the specific task of crack key point feature extraction, named a feature filtration network. The accuracy of the model recognition is controllable and can meet both the pixel-level requirements and the efficiency needs of engineering. In order to verify the rationality and applicability of the image crack detection model in this study, we propose a distribution map of distance. The results for factors of a classical evaluation such as accuracy, recall rate, F1 score, and the distribution map of distance show that the method established in this research can improve crack detection quality and has a strong generalization ability. Our model provides a new method of crack detection based on computer vision technology. crack detection; deep convolutional neural network; object detection; crack key point; fusion and filtration of features +
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researchers, using these latest machine learning algorithms, have continued to propose novel image crack recognition models [14; 15; 16; 17]. Relying on manual extraction of the characteristics of cracks to realize the crack detection of an image cannot meet the needs of a project due to the complex information contained in the actual crack images. In image- and video-based ML approaches for structural health monitoring, differences in illumination, rotation, and the angle of the camera can significantly affect the final results [18; 19]. To meet the requirements for crack detection in practical engineering, automatic learning algorithms based on crack features, especially deep convolutional neural network algorithms, have become a research hotspot. These methods eliminate the first image processing step of most traditional methods, and, based on original crack images, can directly extract crack features and detect cracks through automatic learning models. [PERSON] [20] first proposed the concept of deep learning, which has gained extensive attention in the machine learning area. Models based on deep learning began to emerge [21; 22; 23]. Using the same dataset, [PERSON] [24] compared the concrete crack detection results of classic edge detection with a deep convolutional neural network (DCNN). The results showed that DCNN had advantages in terms of accuracy, detection speed, and resolution. Based on the image segmentation algorithm, many detection methods have been proposed [25; 26; 27; 28]. Those methods with high accuracy obtain a good detection effect, especially for crack width. However, the above methods are all based on image segmentation algorithms, which require a huge amount of work of pixel-level marking on pictures yet still do not reach the expected accuracy in some cases. A large number of diverse crack training samples are also usually required to achieve better detection results [29]. In recent years, object detection methods based on points have been emerging. [PERSON] [30] modeled an object as a single point. [PERSON] [31] raised corner detection, while [PERSON] [32] established a method through center pooling and cascade corner pooling, three of which have inspired the research on crack detection method based on key points. Although the method [PERSON] [33] established is still based on pixels, the detection result is more targeted at predicting crack areas rather than pixels. This method has been an inspiration for further research on crack detection based on crack key points that is effective and suitable for engineering. Crack detection methods based on deep learning depend on the extensivity of the training set and the validity of their algorithm.