## 4 Research on Image Acquisition and Theoretical Methods  
### Depth Image Acquisition  
Camera calibration is the initial and crucial step in acquiring post-blast images at various depths within the tunnel. This is because camera calibration plays a vital role in the 3D reconstruction process, as its results directly impact the accuracy of the reconstructed object. During the process of capturing post-blast images at different depths within the tunnel, the initial and crucial step is camera calibration. This step is essential because camera calibration is fundamental to the 3D reconstruction process; its outcomes directly influence the accuracy of the reconstructed objects. During the manufacturing of cameras, deviations in precision and component installation can lead to lens distortions, which subsequently cause image distortions. These image distortions can be classified into two primary types: pillow-shaped distortion and barrel-shaped distortion (as illustrated in Figure 2) [33]. Correcting these distortions, which are caused by lens aberrations, significantly improves the quality of stereo matching. Without such correction, straight lines in the real world may appear wavy or distorted in the images captured by the camera [34]. Fortunately, specialized calibration plugins are available to correct these types of aberrations, ensuring higher image accuracy. This correction process not only enhances the visual fidelity of the captured images but also plays a pivotal role in accurate 3D modeling and analysis, which are critical for assessing post-blast scenarios. Consequently, thorough and precise camera calibration becomes indispensable in the acquisition of reliable and accurate data for subsequent analysis and interpretation. An experimental camera should first be used to capture images of a black-and-white checkerboard grid to calibrate the camera. This calibration process can be executed using Matlab's self-calibration function. We need to input the checkerboard grid image and provide the physical dimensions of the checkerboard squares, along with other necessary calibration parameters. The system will then automatically detect the corners of the checkerboard grid and proceed with the calibration. Upon completion of the calibration, several key metrics can be obtained, including the reprojection error for each corner in the  
Figure 2: Pillow distortion and barrel distortion. current image (Figure 3a), the pixel variations in the image before and after calibration (represented by different colored lines in Figure 3b, which indicate the image quality before and after calibration), the Euclidean distances between the detected corner points and their corresponding projected coordinates (Figure 3c), and the attitude map of the camera's coordinate system relative to the checkerboard grid (Figure 3d). These metrics are crucial for assessing the accuracy and reliability of the camera calibration, ensuring that the camera is properly calibrated for precise 3D reconstruction and analysis. By using these detailed calibration results, we can fine-tune the camera settings and improve the overall quality of the captured images, which is essential for accurate and reliable post-blast analysis [35; 36]. Figure 3: (**a**) Reprojection error; (**b**) Euclidean distance; (**c**) Pixel Change Difference; (**d**) Chessboard position. (1) The significant effect of tunnel explosions on the quality of depth image acquisition of the profile surface and palm face is vital to the accuracy of the 3D point cloud data model. This process is critical in the postprocessing of relevant data, thereby making the image acquisition phase the most pivotal part of the entire experiment. The experiment employs a Nikon Z50 mirrorless camera equipped with a 50-80 mm lens, a portable explosion-proof industrial-grade mine searchlight, and an industrial tape measure for capturing images. These tools are essential for ensuring high-quality image acquisition, which is a fundamental step in creating an accurate 3D model.