## 2 Related Works and Foundations  
Because the environments of most deep ground water pressure crack stress detection methods are very narrow and the camera field of view is also very narrow, there is an urgent need for the fast processing of small field images, as well as a detection method. Our network structure is divided into two parts: the front part is the mapping reconstruction network that is used to solve the distortion; and the back part is the detection network for the identification and crack splicing. Using information from LPMS-ME1 IMU (Alubi, Guangzhou, China), we spliced the coordinates of the pipe cracks with corresponding frames in the time and spatial dimensions. We spliced them with patches and finally formed a 3D system for flaw detection and crack trajectory generation. In order not to be disturbed by the magnetic field, we shielded the magnetic field information in the Kalman filter [17]. Because crack detection itself is a classification problem, after traversing using a patch mechanism, we evaluated its quality with a specific ground truth and finally got the system to accurately identify the crack trend. To ensure the speed of shooting and avoiding overlapping when obtaining the field of view of each frame, we adopted the method of direct shooting. With a fixed-speed propulsion device, we could quickly and accurately obtain the hole-wall information of the field of view of the front view. Based on the shooting principle of a CCD HD-2M camera (Imperx, Boca Raton, FL, USA), the corresponding sizes of pixels at different positions in the field of view were deviated. The exposure curve will also be distorted due to the different distances from the light source. Using the nonlinear fitting features of the neural network and our homemade feature stickers, we trained a visualization network to achieve radial distortion correction of the in-hole wall-facing image to become a plane image. In order to shoot effectively, we made a corresponding hardware system. With a large amount of calibrated data, the training model had a higher objectivity than a single subjective discrimination. To ensure that the probe rod entered the probe site at a constant speed, a lifting and limiting mechanism was designed. Figure 1 shows the overall and partial introduction of the hardware of our entire system. To consider the actual size relation of each pixel in the image and prevent large errors in the process of the distortion correction, we used a constant-speed propulsion device to propel the whole system. In order to keep the whole system stable, we designed two kinds of stabilizing devices, as shown in Figure 1. An H1S2T motor (NiMotion, Beijing, China) was used to push the screw and drive the lifting platform to feed the test rod evenly. A 485 transmission protocol was adopted for coordinate and visual signal modulation of this system, which was conducive to long-distance transmission. Figure 2 shows a schematic diagram of the actual operation of the system. At present, the crack detection in the inner wall of a pipeline is mainly done through manual data analysis, and it often takes a long time to take pictures inside a pipeline. The influence of different cracks is also different. In the previous article, we mentioned some mainstream crack detection methods, but the detected cracks are not further processed and their morphological characteristics are not separated. If the crack distribution in the hole wall can be obtained succinctly and quickly, the analysis of internal holes will be very convenient. The purpose of this study was to detect the distribution of stress cracks  
Figure 1: System partial function introduction. due to water fracturing on the inner wall of a microbore and the distribution trend of stress in deep ground at a low cost.