This paper presents a deep learning-based autonomous tennis ball retrieval robot designed to address the inefficiencies of manual ball collection. The robot integrates a Raspberry Pi 5B, STM32RCT6 microcontroller, USB camera, and brushless DC motors. Combining a lightweight YOLOv11, an improved DBSCAN clustering-based path planning algorithm, a dual-loop PID controller, and a roller-based collection mechanism, the robot achieves efficient tennis ball recognition, optimized path planning, and autonomous retrieval. The YOLOv11 model was lightened using a StarNet backbone, C3k2_Faster module, and shared convolutional lightweight detection head, significantly reducing computational demands. Experimental results show an 80.8% reduction in parameters, a GFLOPs of only 1.7, an mAP@0.75 of 0.980 6, and a detection speed of 129.7 fps. The DBSCAN-based path planning, optimized through density clustering and a distance-weighted model, enhances the robot′s adaptability and robustness in complex environments. Deployed on a Raspberry Pi, the system accurately recognizes tennis balls under varying lighting conditions, achieves a detection speed of 9~12 fps, and retrieves 7~9 balls per run, demonstrating significantly improved retrieval efficiency and promising practical applications.