Visual Detection Algorithm for Enhanced Environmental Perception of Unmanned Surface Vehicles in Complex Marine Environments

• Published in: Journal of intelligent & robotic systems Vol. 110; no. 1; p. 1
• Main Authors: Dong, Kaiyuan, Liu, Tao, Zheng, Yan, Shi, Zhen, Du, Hongwang, Wang, Xianfeng
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Buoys; Casualties; Classification; Computer peripherals; Control; Datasets; Deep learning; Electrical Engineering; Engineering; Feature maps; Marine environment; Maritime industry; Mechanical Engineering; Mechatronics; Perception; Power; Regular Paper; Robotics; Surface vehicles; Target detection; Unmanned vehicles; Feature fusion network; Unmanned surface vehicles; Sea surface object detection; You only look once; Spatial pyramid pooling
• ISSN: 0921-0296; 1573-0409
• DOI: 10.1007/s10846-023-02020-z

Unmanned surface vehicles (USVs) are distinguished by their intelligence, compactness, and absence of human casualties, making them a vital component of the maritime industry. The implementation of vision-based algorithms for sea surface target detection can enhance the autonomous perceptual abilities of USVs. In the present study, a sea surface target detection algorithm was proposed that fulfils the requirements of USVs marine environment sensing and sea area monitoring. Sea surface target detection faces unique challenges, such as highly variable target sizes and a complex and changing marine environments. The current state-of-the-art You Only Look Once (YOLO) model was selected as the baseline target detection model. Firstly, to improve the network’s ability to extract features of different sizes, a Cross Stage Partial Lightweight Spatial Pyramid Pooling-Fast (CSPLSPPF) structure was proposed. Additionally, for achieving the advantages of multiple feature maps to complement each other and output more judgmental feature maps, Path Aggregation Network Powerful (PANP) was proposed to more rationally fuse features of feature maps with different resolutions. Finally, lightweight convolution with fused attention(LCFA) was proposed to enable the network to selectively focus on crucial spatial and channel information while simultaneously reducing the model’s parameter count. Experiments were conducted on a self-made Ocean Buoys dataset and the open-source Seaships dataset. The results showed that the proposed method could efficiently and accurately detect objects such as ships and buoys in marine environments, which was of significant value for USVs to achieve intelligent environment perception.