## 6 Conclusions  
Aiming at the problems that anchor-rod recognition is difficult, and the positioning accuracy cannot be guaranteed due to the low illumination and uneven lighting in a coal-mine roadway, we propose an anchor identification and positioning method based on the fusion of image enhancement and multiattention mechanism that improved the YOLOv7 model. The process takes the roadway-supported anchor rods as the recognition and positioning target. It uses the improved YOLOv7 target-detection model to identify the anchor rods, which effectively solves the influence of underground illumination on the recognition accuracy of the supported anchor rods and improves the recognition accuracy. The method of depth map combined with least square linear fitting is used to locate the anchor rods, which provides an effective way for the accurate positioning of the anchor rods in the coal-mine roadway. (1) The proposed image enhancement and multiattention mechanism fusion improved the YOLOv7 target-detection model, which can effectively reduce the difficulty of target recognition caused by illumination problems, enhance the ability to extract small target features, and improve recognition accuracy;  
(2) A multiview vision sensor is used to collect sample images in simulated roadways and is combined with the anchor-rod data of actual coal-mine roadways, which enhances the diversity of the samples and the adaptability of the model to the supported anchor rods of the actual underground roadway and establishes the image dataset of the anchor-rod target detection under the environment of low and uneven illumination;  
(3) The image is enhanced by the Contrast-Limited Adaptive Histogram Equalization algorithm, which reduces the influence of the illumination problem in anchor-rod recognition, improves the clarity of target contour and features, weakens noise interference, and improves image quality;  
(4) Based on the YOLOv7 model, BiFormer and CBAM are added to improve the detection ability of the model for small targets and the attention to essential features and reduce the noise interference of the environment;  
(5) The method of extracting ROI combined with median and bilateral filtering is used to denoise the depth map, which solves the problem of effective information loss caused by noise. At the same time, the processing time and calculation amount are reduced, which is beneficial to extract the depth value of the target point;  
(6) Experiments verify the proposed recognition and positioning method of the supported anchor rod. The experimental results show that the mAP of the improved model is 5.7% higher than that of the original model, reaching 94.9%. The model can identify the supported anchor rod accurately. The error between the positioning coordinates of the target point on each axis and the measured coordinates is no more than 11 mm, which  
Figure 17: Coordinate error curve. meets the positioning-error requirements of the anchor rod in the drilling and anchoring operation. It shows that the positioning method can ensure the positioning accuracy of the anchor rod and provide a basis for the positioning of the body of the drill-anchor robot. Software, Validation, E.Z. ; Investigation, C.W. ; Data curation, X.Y., Y.Q. ; Writing--original draft, J.Y. ; Writing--review & editing, X.X. ; Supervision, Q.M. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant Nos. 52374161, 51834006); the National Key Research Development Program Young Scientists Project of China (No. 2022 YFF0605300); Xi'an Science and Technology Plan Project (No. 22 GXFW0067); National Key Research Development Program of China (No. 2023 YFC2907603); and Shaanxi Province \"Two Chain\" Integrated Enterprise (Institute) Joint Project (No. 2023-LL-QY-03). Not applicable. The supported anchor-rod dataset presented in this article is not publicly available due to the data being part of ongoing research.