Deep learning-based object detection in maritime unmanned aerial vehicle imagery: Review and experimental comparisons

被引:27
作者
Zhao, Chenjie [1 ,2 ,3 ]
Liu, Ryan Wen [1 ,2 ,3 ]
Qu, Jingxiang [1 ,2 ]
Gao, Ruobin [4 ]
机构
[1] Wuhan Univ Technol, Sch Nav, Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Peoples R China
[2] State Key Lab Maritime Technol & Safety, Wuhan 430063, Peoples R China
[3] Wuhan Univ Technol, Chongqing Res Inst, Chongqing, Peoples R China
[4] Nanyang Technol Univ, Sch Civil & Environm Engn, Singapore, Singapore
关键词
Maritime industry; Unmanned aerial vehicle; Maritime UAVs; Object detection; Aerial datasets; CONVOLUTIONAL NEURAL-NETWORK; UAV; MULTISCALE; TRACKING; FUSION;
D O I
10.1016/j.engappai.2023.107513
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at https://github.com/zcj234/MS2ship.
引用
收藏
页数:21
相关论文
共 173 条
[71]   Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition [J].
Lin, Ming ;
Wang, Pichao ;
Sun, Zhenhong ;
Chen, Hesen ;
Sun, Xiuyu ;
Qian, Qi ;
Li, Hao ;
Jin, Rong .
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), 2021, :337-346
[72]   ECascade-RCNN: Enhanced Cascade RCNN for Multi-scale Object Detection in UAV Images [J].
Lin, Qizhang ;
Ding, Yan ;
Xu, Hong ;
Lin, Wenxiang ;
Li, Jiaxin ;
Xie, Xiaoxiao .
2021 7TH INTERNATIONAL CONFERENCE ON AUTOMATION, ROBOTICS AND APPLICATIONS (ICARA 2021), 2021, :268-272
[73]   Focal Loss for Dense Object Detection [J].
Lin, Tsung-Yi ;
Goyal, Priya ;
Girshick, Ross ;
He, Kaiming ;
Dollar, Piotr .
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2017, :2999-3007
[74]   Microsoft COCO: Common Objects in Context [J].
Lin, Tsung-Yi ;
Maire, Michael ;
Belongie, Serge ;
Hays, James ;
Perona, Pietro ;
Ramanan, Deva ;
Dollar, Piotr ;
Zitnick, C. Lawrence .
COMPUTER VISION - ECCV 2014, PT V, 2014, 8693 :740-755
[75]   Feature Pyramid Networks for Object Detection [J].
Lin, Tsung-Yi ;
Dollar, Piotr ;
Girshick, Ross ;
He, Kaiming ;
Hariharan, Bharath ;
Belongie, Serge .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :936-944
[76]   Lightweight object detection algorithm for robots with improved YOLOv5 [J].
Liu, Gang ;
Hu, Yanxin ;
Chen, Zhiyu ;
Guo, Jianwei ;
Ni, Peng .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 123
[77]   DBF-YOLO: UAV Small Targets Detection Based on Shallow Feature Fusion [J].
Liu, Haiying ;
Duan, Xuehu ;
Chen, Haonan ;
Lou, Haitong ;
Deng, Lixia .
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2023, 18 (04) :605-612
[78]   Intelligent Edge-Enabled Efficient Multi-Source Data Fusion for Autonomous Surface Vehicles in Maritime Internet of Things [J].
Liu, Ryan Wen ;
Guo, Yu ;
Nie, Jiangtian ;
Hu, Qin ;
Xiong, Zehui ;
Yu, Han ;
Guizani, Mohsen .
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, 2022, 6 (03) :1574-1587
[79]   Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images [J].
Liu, Siyu ;
Cheng, Jian ;
Liang, Leikun ;
Bai, Haiwei ;
Dang, Wanli .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 :8287-8296
[80]   Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection [J].
Liu, Wei ;
Hasan, Irtiza ;
Liao, Shengcai .
PATTERN RECOGNITION, 2023, 135