Real-Time Underwater Vision Sensing System for AUV Tracking

With the continuous development of emerging technologies such as big data and artificial intelligence, the related technologies of perception tasks on which underwater object tracking relies have made great progress. However, a significant barrier still exists in implementing high-performance real-time underwater object tracking on low-power edge devices. To achieve real-time tracking of underwater objects for edge devices, this article develops an underwater real-time visual sensing system applied to AUV tracking. First, an underwater object tracking device is designed in this article employing a stereo binocular camera, an edge embedded NVIDIA Jetson Xavier NX and an STM32 control board. Then, after preprocessing, the input image with the effective USM algorithm, we propose a quick approach for detecting underwater objects based on SIoU-YOLOv8n, which enables automatic object recognition and selection. At the same time, this article proposes a twin network UW-Siam for continuous tracking of underwater objects, which achieves more accurate underwater object tracking. Finally, the algorithm is deployed to the designed real-time underwater vision sensing system and tested in real-world scenarios. The tracking accuracy reached 0.652, and the detection mAP reached 0.97. The results indicate that the system can rapidly detect and continuously monitor objects, performing well in real-world scenarios with high accuracy and robustness.