# Crack Detection of Concrete Based on Improved CenterNet Model
## 1 Introduction  
With the rapid development of China's economy, civil engineering construction projects are increasing, and as one of the pillar industries of the economy, the construction industry has played an irreplaceable role in national construction. With the increasing number of buildings, roads, bridges, tunnels, and other infrastructures, maintaining them in good working condition is extremely important for public safety. Concrete cracks are usually caused by internal stress and environmental action, leading to the internal fatigue of the material and resulting in cracks and fractures on the surface of the concrete [1]. The occurrence of cracks often represents a change in the structure where the cracks occur. Over time, further cracking and falling off often occur, and water seepage occurs. Therefore, crack detection is of great significance for the healthy operation of construction projects [2]. Based on the location of cracks in the material, they can be divided into surface and internal cracks. The main research object of this study was the surface cracks in construction engineering concrete. Surface crack detection methods include eye observation, ultrasonic detection [3], eddy current detection [4], speckle interference [5], penetration detection [6], laser holography [7], X-ray detection [8], and computer vision detection [9]. Most of the aforementioned methods have formed a relatively complete detection system that can perform surface crack detection well; however, they also have their adaptation scenarios and shortcomings. For example, although ultrasonic detection is sensitive to planar defects, it is difficult to detect nonplanar structures owing to acoustic coupling, and the surface crack detection effect of arched structures facing some projects could be better. Although the detection accuracy is high, optical detection is significantly affected by ambient light interference and vibrations duringactual operation. The infrared detection method has a fast detection speed; however, the detection environment is limited due to the equipment's large size. Current computer vision detection technology often obtains the surface image or video of the research object through a camera and other sensing equipment; then, the obtained image or video is pre-processed and feature extracted, and different algorithm models are trained and tested to finally achieve the purpose of target recognition or positioning [10]. In the use of deep learning methods to deal with crack detection problems, researchers generally used image processing algorithms to make predictions. [PERSON] [11] studied the problem of crack detection using a mathematical morphology and image fusion. [PERSON] [12] conducted a study of road crack detection and evaluation using computer vision. Tongji University studied an MTI-100 tunnel-detection system and achieved crack detection and location [13]. [PERSON] [14] used convolutional neural networks to detect the surface cracks. Using an adaptive iterative method, [PERSON] [15] used an improved Otsu threshold segmentation algorithm to study crack images. [PERSON] [16] proposed a new image analysis method for concrete crack detection and conducted a detailed study of a detection method based on edge cracks. [PERSON] [17] completed the study of visual feature maps and proved that the neural network can effectively distinguish the difference between the features of split and non-crack images, which led to significant progress. [PERSON] [18] conducted an analysis of existing deep learning object detection algorithms, obtained the structure and design method of the crack detection network based on convolutional neural network, and realized the image crack detection based on DeepLabV3+. [PERSON] [19] proposed an automatic crack detection method for concrete Bridges based on machine vision, which realized the automatic identification and measurement of cracks and improved the detection efficiency.