# Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft
## 1 Introduction
[18] and [PERSON] et al. [19] used yo-based object detection to extract the bounding box of cracks for the first time. Subsequently, the former uses a morphological algorithm to pixellate defects, while the latter uses a mask filter to eliminate statistical noise and the global threshold connection component optimization method to highlight cracks from the background. [PERSON] et al. [20] established a new model CNN (faster R-CNN) based on region, which can be used to detect five different damage types, such as concrete cracks, two-stage (medium and high) steel corrosion, bolt corrosion and steel delamination. To realize the automatic identification of concrete cracks, [PERSON], [PERSON] and [PERSON] [21] applied a region-based fast convolution neural network (faster R-CNN) to the real image of concrete bridges, which can automatically locate cracks from the original image, thus realizing the automatic identification of concrete cracks. [PERSON] et al. [22] proposed a crack segmentation algorithm based on an attention mechanism and multi-scale feature fusion, which marked various cracks in different road backgrounds. [PERSON] [23] and others put forward a six-layer CNN to detect and characterize road cracks, which can improve the accuracy of road crack detection. The scheme proposed by [PERSON] et al. [24] adopted image processing as the preprocessing and post-processing of a 1D DL model. As the predecessor of DL, image-processing-aided DL eliminates labor-intensive marks and a plane structure background without any distinguishing features during DL training and testing. The model identifies potential crack candidate areas. [PERSON] et al. [25] put forward a new statistical crack quantification algorithm and verified the automatic evaluation of concrete structure crack width and length through experiments. The quantitative crack evaluation results of width and length were successfully compared with the actual field measurement values, with average differences of 18.07% and 26.28%, respectively. However, the existing visual inspection technology mostly focuses on the processing of a single defect image of the borehole wall, and its adaptability is poor. Moreover, the systematic study of shaft lining damage is insufficient, and the position estimation of damage is also lacking, so that accurate position information can not be obtained. Although there is research on the quantitative estimation of shaft lining defect damage using visual image processing, a complete detection system has not been formed, and the complete detection process of qualitative identification, accurate positioning and quantitative estimation of shaft lining defects cannot be realized. In this paper, the CNN model of shaft wall image defect identification is constructed using the deep learning method to realize the accurate classification of shaft wall images. Then, combined with the structural characteristics of a rack-and-rail inspection system, an estimation method of the shaft wall defect position based on robot posture is proposed. After a series of image processing, the key characteristic parameters, such as crack width and length, are obtained by quantitative analysis, thus forming a complete detection system. According to the current research, combined with the importance of wind shaft inspection, and considering that the main inspection method of wind shafts is still manual inspection, detection and imaging are facing challenges. In addition, the cracks on the shaft wall of the wind tunnel are long and irregular, so a single photo cannot obtain the information of the whole crack. In this paper, a new type of wind shaft inspection robot is proposed, which can be as close as possible to the shaft wall to ensure the clarity of the inspection image, and on the other hand, it can move according to the direction of the crack to ensure that the information of the whole crack can be obtained at one time. The first part of this paper introduces the challenges and solutions faced by the wind shaft wall inspection.