###### Abstract  
The drilling robot is the key equipment for pressure relief in rockburst mines, and the accurate recognition of a pressure relief hole is the premise for optimizing the layout of pressure relief holes and intelligent drilling. In view of this, a pressure relief hole recognition method for a drilling robot, based on single-image generative adversarial network (SinGAN) and improved faster region convolution neural network (Faster R-CNN), is proposed. Aiming at the problem of insufficient sample generation diversity and poor performance of the traditional SinGAN model, some improvement measures including image size adjustment, multi-stage training, and dynamically changing iteration times are designed as an improved SinGAN for the generation of pressure relief hole images. In addition, to solve the problem that the traditional depth neural network is not ideal for small-size target recognition, an improved Faster R-CNN based on multi-scale image input and multi-layer feature fusion is designed with the improved SqueezeNet as the framework, and the sample data collected from ground experiments are used for comparative analysis. The results indicate that the improved SinGAN model can improve the diversity of generated images on the premise of ensuring the quality of image samples, and can greatly improve the training speed of the model. The accuracy and recall rate of the improved Faster R-CNN model were able to reach 90.09% and 98.32%, respectively, and the average detection time was 0.19 s, which verifies the superiority of the improved Faster R-CNN model. To further verify the practicability of the proposed method, some field images were collected from the underground rockburst relief area in the coal mine, and a corresponding test analysis was carried out. Compared with three YOLO models, the accuracy and recall rate of improved Faster R-CNN model improved significantly, although the training time and recognition time increased to a certain extent, which proves the feasibility and effectiveness of the proposed method. drilling robot; pressure relief holes recognition; generative adversarial network; lightweight convolutional neural network; faster R-CNN 20231351335133  
Many scholars have conducted in-depth research on hole recognition in different fields. [PERSON] et al. [6] proposed a target plate double hole detection method based on the improved maximum between class variance method, which not only shortens the calculation time, but also improves the accuracy of bullet hole center positioning. [PERSON] et al. [7] developed an electric vehicle charging hole recognition and positioning system, based on the principle of binocular vision imaging, using Halcon image processing software. [PERSON] et al. [8] proposed a hole location and normal vector detection algorithm based on template matching, which converts a three-dimensional space point cloud into a two-dimensional plane point cloud through binarization, uses template matching algorithm and region of interest to identify and locate holes in two-dimensional plane, and indirectly calculates the hole location according to the point cloud in the hole neighborhood. [PERSON] et al. [9] presented an industrial robot alignment technology based on monocular vision. The pose of the workpiece was is obtained through the monocular vision system, which realized the accurate alignment of the alignment axis at the end of the robot and the workpiece target. [PERSON] et al. [10] proposed a method for machining feature recognition of thin-walled multi-cavity structural parts based on the combination of rules, layers, and feature suppression. [PERSON] et al. [11] proposed an automatic positioning and recognition method for a container key-hole based on machine vision, which meets the real-time requirements of port automation. It can be seen from the above reports that traditional target recognition methods often use artificial features such as HOG features, SIFT features, and HAAR features to extract target features [12; 13; 14].