# Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft
## 6 Discussion and Conclusions  
Aiming at the problems of poor working environments, high operation risks and low detection effects existing in the regular manual inspection and maintenance of coal mine air shafts, combined with the provisions of the \"Coal Mine Safety Regulations\", a new type of air shaft inspection robot and shaft wall detection method are gradually developed. Firstly, this paper analyzes the difficulties of stable imaging and the inability to obtain complete fracture images in the shaft wall detection of wind wells. The mapping relationship between the concentration of water vapor and dust in the air shaft and image clarity is determined by experiments. Then, the inspection robot is designed to move along the axial and circumferential directions, close to the shaft wall, and the specific structures of the inspection module, moving module and lifting module of the inspection robot are determined according to the overall scheme. Compared to other inspection robots in [6; 7; 8; 9; 11; 12], the inspection robot designed in this paper has good controllability and stable movement and speed performance. Aiming at the difficulty of long-distance communication in the vertical direction of the shaft, the communication module adopts the communication mode of combining wired communication with wireless communication, which can realize long-distance stable communication in the vertical direction of air shafts. The power supply module combines a wired power supply with wireless power supply, which ensures the high endurance of the robot. These are the characteristics that show that the specific structural module of the patrol robot designed in this paper is different from other patrol robots, such as those in [7; 8; 9; 10]. Secondly, the systematic research on the robot-assisted visual inspection method of the shaft lining condition is completed. Considering that the visual inspection of the shaft lining condition is not specific enough, and the detection of crack defects focuses on concrete, roads [24] and bridges [14], as mentioned in [20; 21; 25], the nondestructive inspection technology based on vision is rarely used to detect shaft lining cracks. Therefore, by using the characteristics of [13; 20; 21; 24] for reference, a CNN model for the defect recognition of shaft wall images is constructed, and the accurate classification of shaft wall images (health, cracks and spalling) is realized, with a recognition accuracy as high as 97.1%. Then, combined with the structural characteristics of the rack guide rail detection system, a method for estimating the position of drilling defects based on robot posture is proposed. After a series of image processing, the key characteristic parameters such as crack width and length are obtained through quantitative analysis. Finally, the experimental results show that the damage identification accuracy of the detection system can reach above 94.4%, and the quantitative detection accuracy of crack defects can reach above 92%. \begin{table}
\begin{tabular}{c c c c} \hline \hline Treatment result & & & \\ \hline Length detection value/mm & 243.88 (696.8 pixel) & 336.12 (819.8 pixel) & 426.37 (926.9 pixel) \\ Measured value of & 262.2 & 350.2 & 450.7 \\ length/mm & 93.0\% & 96.0\% & 94.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._In addition, the inspection robot in this paper not only improves the efficiency and accuracy of mine shaft wall detection, but also provides reference for subsequent research, which is of extraordinary significance to ensure long-term safe production in coal mines. Although the test system can detect the shaft wall of air shafts through experiments, it has not been applied to the actual detection of air shafts in coal mines. However, there is a certain theoretical basis for the design of the overall detection system and the systematic study of the visual detection method of shaft wall conditions.