## 1 Introduction  
The advancement of science and technology has facilitated the growth of artificial intelligence and machine vision technology, thereby enabling the development of a vision-guided robot drilling control system [1, 2]. The integration of machine vision technology enables tunnel drilling robots to achieve precise positioning and efficient hole drilling, thereby markedly enhancing construction efficiency and quality while satisfying the rigorous standards of modern tunnel construction for superior quality and efficiency [3, 4, 5]. Nevertheless, the extant hand-eye integration tunnel drilling robot continues to encounter numerous challenges in practical deployment. The camera is typically situated within the robotic arm used for drilling, which is vulnerable to the disruption caused by dust within the tunnel during the drilling process. This results in a deterioration of image acquisition quality, consequently affecting the stability and accuracy of the visual recognition system.  
the lengthy nature of the tunnel project necessitates the drilling of numerous holes within the tunnel area. The conventional approach of utilizing a robotic arm to repeatedly identify and locate these holes is not only time-consuming but also increases the cost and complexity of the operation.  
To overcome the aforementioned challenges, a hand-eye separation-based tunnel drilling robot hole location localization system was constructed, based on the first-frame localization and follower tracking method of the drilling robot arm. The optimal configuration for a hand-eye cooperative operation is achieved by mounting a dual robotic arm system with a vision arm (i.e., \"eye\") and a perforating robot arm (i.e., \"hand\") on the same mounting surface. The vision robot arm is tasked with acquiring the positional data of the punching robot arm in real-time, thereby enabling the precise tracking and tracing of the punching robot arm through the utilization of a high-precision follower adjustment mechanism. The schematic diagram is illustrated in Figure 1.  
In the hole localization system of a tunnel boring robot, the accuracy of the initial frame localization has a significant influence on the subsequent follow-up tracking process. An error in the initial frame localization may fail in the subsequent tracking process, thereby negatively impacting the overall quality of the task completion. Additionally, it should be noted that the tunnel drilling robot hole positioning system, which is based on hand-eye separation, must be installed on the edge intelligent terminal industrial control machine. This results in a limitation of system resources. It is therefore important to minimize the computational complexity, memory occupation, and energy consumption problems while ensuring the positioning accuracy of the first frame. To guarantee the precision of identifying and positioning the perforated robotic arm, this study employs a deep learning-based target detection algorithm, RFBNet [6], which modifies its network structure and loss function to enhance the accuracy and efficiency of the first-frame localization. Furthermore, in the context of continuous tracking in complex environments, the tracked target frequently encounters the challenge of occlusion, which may result in the drift phenomenon of the tracker and consequently impair the accuracy and stability of continuous tracking. Furthermore, during the follow-along tracking process of the perforating robotic arm, the vision robotic arm may encounter issues such as target loss  
Figure 1: Hand–eye separation schematic.