## 1 Introduction
The above studies have proposed target-recognition methods based on deep learning, but these methods are biased towards coal and gangue recognition, anchor rods in belt foreign bodies, and roadway anchor-hole position detection rather than the actual roadway-supported anchor rods. After accurately identifying the anchor rods, the positioning of supported anchor rods is the crucial problem to realize the positioning of the drill-anchor robot. Suppose the position information of the roadway anchor rods can be detected in real-time and accurately. In that case, it will make the drill-anchor robot complete the drilling and anchoring operation more efficiently and ensure production safety. At present, many scholars have conducted in-depth research on vision-based positioning technology. [PERSON] et al. [16] proposed an improved census transform algorithm to obtain the disparity map of anchor rods and the depth information of the anchor image. They used the minimum circumscribed rectangle and the maximum circumscribed rectangle algorithm to extract the pixel coordinates of the feature points of the bolt, which can accurately obtain the position information of the anchor rods. However, due to the complexity of the calculation process, the running time is too long. [PERSON] et al. [17] used a dynamic template-matching algorithm combined with an RGB-D camera for positioning, which had good positioning accuracy under static and dynamic conditions. However, this method detected a single target and could not meet the requirements of simultaneous positioning of multiple anchor rods in coal-mine roadways. [PERSON] et al. [18] improved the YOLOv5s model, added the SPD-Conv and collaborative attention mechanism, and restored the depth map to realize anchor-hole positioning based on a deep-learning model and depth camera. However, this positioning method relies entirely on vision and deep-learning recognition, and positioning is vulnerable to environment and equipment. [PERSON] et al. (2019) proposed a binocular vision measurement technique using four light spots as feature points for the position of a boring machine under complex backgrounds such as mixed illumination and low illumination. However, the final positioning error of this method is larger than that of other methods, and the positioning accuracy needs to be improved. In summary, we focus on exploring a coal-mine roadway anchor-rod recognition and positioning method based on the fusion of image enhancement and multiattention mechanism to improve the YOLOv7 (the seventh version of the You Only Look Once) model (YOLOv7 + CLAHE + BiFormer + CBAM fusion). This method preprocesses the image obtained by the visual sensor to solve the problems of the traditional way in the low illumination and uneven lighting environment of the coal-mine roadway. By using the deep-learning algorithm, combining the depth image with the RGB image alignment and the least squares linear fitting method, the roadway anchor rod's automatic identification and precise positioning are realized. This method has high efficiency and accuracy and can adapt to the characteristics of roadway anchor rods in different mining areas, which has a strong practicability and popularization value. The structure of the rest of this article is as follows. Section 2 gives the principle and steps of the identification and positioning method. Section 3 mainly introduces the structure and composition of the image enhancement and multiattention mechanism fusion-improved YOLOv7 model proposed in this paper. Section 4 presents the positioning method of the supported anchor rod, including sensor calibration, coordinate transformation, and data processing. The experimental deployment and results are given in Section 5, and the work of this paper is summarized in Section 6.