An Underwater Object Recognition System Based on Improved YOLOv11

Abstract

Common underwater target recognition systems suffer from low accuracy, high energy consumption, and low levels of automation. This paper introduces an underwater target recognition system based on the Jetson Xavier NX platform, which deploys an improved YOLOv11 recognition algorithm. During operation, the Jetson Xavier NX invokes an industrial camera to capture underwater target images, which are then processed by the improved YOLOv11 network for inference. The recognized information is transmitted via a serial port to an STM32 control board, which adaptively adjusts the lighting system to enhance image clarity based on the target information. Finally, the system controls an actuator to release a buoyant ball with positioning capabilities and communicates with the shore. On the ROUD dataset, the improved YOLOv11 algorithm achieves an accuracy of 87.5%, with a parameter size of 2.58M and a floating-point operation count of 6.3G, outperforming all current models. Compared to the original YOLOv11, the parameter size is reduced by 5% and the floating-point operation count by 0.3G. The improved DD-YOLOv11 also shows good performance on the URPC2020 dataset. After on-site experiments and hardware–software integration tests, all functions operate normally. The system is capable of identifying a specific underwater target with an accuracy rate of over 85%, simultaneously releasing communication buoys and successfully establishing communication with the shore base. This indicates that the underwater target recognition system meets the requirements of being lightweight, high-precision, and highly automated.