Research on Key Technology of Ship Re-Identification Based on the USV-UAV Collaboration
Author:
Dou Wenhao1ORCID, Zhu Leiming1, Wang Yang1, Wang Shubo2ORCID
Affiliation:
1. School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518071, China 2. School of Automation, Institute of Intelligent Unmanned System, Qingdao University, Qingdao 266071, China
Abstract
Distinguishing ship identities is critical in ensuring the safety and supervision of the marine agriculture and transportation industry. In this paper, we present a comprehensive investigation and validation of the progression of ship re-identification technology within a cooperative framework predominantly governed by UAVs. Our research revolves around the creation of a ship ReID dataset, the creation of a ship ReID dataset, the development of a feature extraction network, ranking optimization, and the establishment of a ship identity re-identification system built upon the collaboration of unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). We introduce a ship ReID dataset named VesselID-700, comprising 56,069 images covering seven classes of typical ships. We also simulated the multi-angle acquisition state of UAVs to categorize the ship orientations within this dataset. To address the challenge of distinguishing between ships with small inter-class differences and large intra-class variations, we propose a fine-grained feature extraction network called FGFN. FGFN enhances the ResNet architecture with a self-attentive mechanism and generalized mean pooling. We also introduce a multi-task loss function that combines classification and triplet loss, incorporating hard sample mining. Ablation experiments on the VesselID-700 dataset demonstrate that the FGFN network achieves outstanding performance, with a Rank-1 accuracy of 89.78% and mAP of 65.72% at a state-of-the-art level. Generalization experiments on pedestrian and vehicle ReID datasets reveal that FGFN excels in recognizing other rigid body targets and diverse viewpoints. Furthermore, to further enhance the advantages of UAV-USV synergy in ship ReID performance, we propose a ranking optimization method based on the homologous fusion of multi-angle UAVs and heterologous fusion of USV-UAV collaborative architecture. This optimization leads to a significant 3% improvement in Rank-1 performance, accompanied by a 73% reduction in retrieval time cost.
Funder
Marine Economy Development Project of Guangdong Province Science and Technology Project of Shenzhen CCF-Baidu Apollo Joint Development Project Fund
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
Reference59 articles.
1. Zhang, Z., Ni, G., and Xu, Y. (2020, January 11–13). Trajectory prediction based on AIS and BP neural network. Proceedings of the 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China. 2. Zhao, L., Yang, J., and Shi, G. (2017, January 22–24). A Correction Method for Time of Ship Trajectories Based on AIS. Proceedings of the 1st International Conference on Big Data Research, Osaka, Japan. 3. A discriminatively learned CNN embedding for person reidentification;Zheng;ACM Trans. Multimed. Comput. Commun. Appl. (TOMM),2017 4. Li, J., Wang, J., Tian, Q., Gao, W., and Zhang, S. (November, January 27). Global-local temporal representations for video person re-identification. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea. 5. Progressive learning with multi-scale attention network for cross-domain vehicle re-identification;Wang;Sci. China Inf. Sci.,2022
|
|