## 1 Introduction  
The use of anchor supports in coal mine roadways plays a crucial role in enhancing roadway stability and ensuring worker safety and efficient production [1, 2]. As a critical component of the anchor support system, the steel belts that are used feature evenly distributed anchor holes. During the support process, anchor bolts pass through the anchor holes in the steel belt and are inserted into holes in the surrounding rock of the roadway. These anchor bolts are then secured to the surrounding rock using bolts [3, 4]. This design effectively disperses and evenly distributes the stress on the surrounding rock, thereby enhancing the support and preventing roadway collapse [5]. The current method used to position anchor holes on steel belts primarily relies on manual determination by workers, resulting in low positioning accuracy and poor support efficiency. Therefore, the precise determination of anchor hole positions is fundamental to improving the efficiency of roadway support. To improve the accuracy of anchor hole positioning and the level of automation in anchor support, experts and scholars have researched automated positioning methods for anchor holes and have made significant progress. The existing research is mainly divided into three kinds of methods: sensor-based positioning, deep learning-based positioning, and vision-based positioning. There has been limited research on sensor-based visual positioning methods for use in the case of anchor holes. [PERSON] et al. [6] proposed a method for the intelligent identification and positioning of steel belt anchor holes in roadway support using inductive sensors, which are unaffected by light and dust; however, the underground environments of coal mines are complex, and the strong electromagnetic interference present in such environments affects the normal working conditions of inductive sensors. A deep learning algorithm is an advanced intelligent algorithm that exhibits high detection accuracy and speed when applied to target recognition and detection [7; 8]. In recent years, scholars have employed this algorithm for the identification, detection, and precise localization of anchor holes. [PERSON] et al. [9] proposed an intelligent real-time detection and location method for anchor holes used in coal mine roadway support based on an improved YOLOv5s model and a depth camera, which detected and located anchor holes in a laboratory-simulated tunnel with a location error of less than 5 mm and an average FPS of 28.9. [PERSON] et al. [10] proposed an improved YOLOv7 model based on the fusion of image enhancement and a multi-attention mechanism. In this case, a self-made dataset was used for testing and training, and the position error was less than 11 mm. The RBF neural network algorithm has been adopted to accurately position boreholes [11], and the results showed that the accuracy of simulated borehole positioning was controlled at 1.5% while the accuracy during the experiment was 5.6%. [PERSON] et al. [12] proposed a deep learning-based identification and localization method for downhole steel belt anchor holes. The localization accuracy of this method depends entirely on the accuracy of the labeled dataset from the principle; therefore, its localization accuracy and stability are difficult to guarantee. In general, anchor hole identification, detection, and localization methods based on deep learning can achieve rapid identification and the precise localization of anchor holes; however, these approaches have certain limitations. Significant variations in coal mine roadway environments, including lighting conditions, the anchor hole shape, and surrounding backgrounds affect the accuracy of deep learning methods when recognizing and locating anchor holes.