The cleaning and maintenance of photovoltaic (PV) panels are critical tasks in the operation of PV power stations. In this context, the PV cleaning shuttle systems equipped with robotic arms and PV cleaning terminals have emerged as an innovative solution. These systems require precise acquisition of PV panel poses, including the tilt angle and distance relative to the vehicle body. To address this issue, this study proposes a monocular-camera-based visual positioning method for PV panels. First, the YOLOv8-pose keypoint detection model is improved to enhance detection accuracy. The PSA mechanism is introduced to optimize the backbone network, while the DySample dynamic upsampling module and proposed ADown* downsampling module are used to strengthen the neck network. Next, by combining the improved YOLOv8-pose with the geometric features of the PV panels, a method for calculating the tilt angle and distance is proposed, thereby achieving the pose positioning of PV panels. Experimental results show that the proposed improved algorithm achieves a 26.2% and 20.1% increase in the accuracy of calculating tilt angles and distances compared to the original YOLOv8-pose, enabling more precise positioning of PV panels.