## 1 Introduction  
Coal-mine production is mainly based on underground mining, and roadway development depends on tunneling operations. Therefore, anchor support is vital in coal-mine production [1; 2]. Improving the efficiency of roadway tunneling has become one of the essential contents of coal-mine intelligence. At present, the main factor affecting the efficiency of roadway excavation is the intelligent level of drilling and anchoring operations. On 2 January 2019, the National Coal Supervision Bureau issued the 'Key R&D Directory of Coal Mine Robots'. On 25 February 2020, the National Development and Reform Commission and eight other ministries and commissions jointly issued the 'Guiding Opinions on Accelerating the Intelligent Development of Coal Mines,' which clarified the development goals and main tasks of coal mine drill-anchor robots. Drill-anchor robots are of great significance in solving the problem of supporting efficiency, but the positioning of drill-anchor robots is mainly done by manual operation, which cannot ensure the accuracy and efficiency of fuselage positioning [3; 4]. The positioning of drill-anchor robots can be solved by taking the supported anchor rods as a reference [5]. However, there are few studies on the identification and location of anchor rods, and the complex roadway environment increases the difficulty of anchor-rod identification [6]. Therefore, an in-depth study of the identification and positioning technology of the anchor rods in the roadway, especially in the environment of low and uneven illumination in coal-mine roadways, is of great significance for the precise positioning of drill-anchor robots [7]. Currently, most studies for object recognition in uneven lighting and low illumination environments are implemented based on machine vision. Aiming at the problem of image enhancement under low illumination, [PERSON] et al. [8] integrated the Swin v2 module and convolution module and adopted the attention mechanism of a multiscale module. A low illumination image-enhancement algorithm based on the Transformer model was proposed to realize the enhancement of low illumination images in a coal mine. Reference [9] used the MobileNetV2 feature-extraction network as the backbone network of DeepLabV3+ and adopted the channel attention (SE) and channel spatial attention (CA) mechanisms to improve the accuracy of coal-rock recognition. [PERSON] et al. [10] added a coordinate attention mechanism module to the backbone network of the YOLOv5s model and increased the clarity of the anchor-hole image through super-resolution reconstruction technology, which effectively improved the accuracy of recognition and classification. [PERSON] et al. [11] added a convolutional block attention model into the YOLOv5 network to enhance the saliency of the target in the image, aiming at the problem that the target cannot be accurately identified due to the uneven illumination environment. [PERSON] et al. [12] proposed an Attention Res-UNet method. U-Net is used for semantic segmentation and uses an attention mechanism to reduce the influence of uneven lighting conditions, which proves its recognition superiority in complex environments. Based on traditional YOLOv5s, [PERSON] et al. [13] proposed an ODEL-YOLOv5s detection model based on deep learning, which increased the three-scale prediction of the model to four-scale prediction and improved the detection ability of the model for small obstacles. [PERSON] et al. [14] proposed an adaptive focusing target feature fusion network based on YOLOX to balance detection speed and detection accuracy for complex underground environments. Based on the MobileNetV3 large module structure, [PERSON] et al. [15] adopted the CBAM attention mechanism module, which can better improve the network's feature-extraction ability of complex pixel information in the regions of interest in coal and gangue images.