We developed an autonomous navigation system for a solar-panel-cleaning vehicle. The system utilizes the YOLOv4-Tiny object detection model to detect white lines on the solar panels and combines the model with a proportional–integral–derivative (PID) controller to achieve autonomous navigation functionality. The main system platform was built on Raspberry Pi, and the Intel Neural Compute Stick 2 (NCS2) was used for hardware acceleration, which boosted the model’s inference speed from 2 to 8 frames per second (FPS), significantly enhancing the system’s real-time performance. By tuning the PID controller parameters, the system achieved an optimal performance, with $K_P$ = 11, $K_i$ = 0.01, and $K_d$ = 30, maintaining the average value of the error e(t) at −0.0412 and the standard deviation at 0.1826 and improving the inference speed. The system autonomously followed the white lines on the solar panels and automatically turned when reaching the boundaries. The system also autonomously cleaned itself. The developed autonomous navigation system effectively improved the efficiency and convenience of solar panel cleaning.