Shao, Zeyuan;
Lyu, Hongguang;
Yin, Yong;
Cheng, Tao;
Gao, Xiaowei;
Zhang, Wenjun;
Jing, Qianfeng;
... Zhang, Lunping; + view all
(2022)
Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment.
Journal of Marine Science and Engineering
, 10
(11)
, Article 1783. 10.3390/jmse10111783.
Preview |
Text
jmse-10-01783-v2.pdf - Published Version Download (87MB) | Preview |
Abstract
Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet * that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model.
| Type: | Article |
|---|---|
| Title: | Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/jmse10111783 |
| Publisher version: | https://doi.org/10.3390/jmse10111783 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Technology, Physical Sciences, Engineering, Marine, Engineering, Ocean, Oceanography, Engineering, autonomous ships, sea-surface, object detection, computer vision, convolutional neural network (CNN), VarifocalNet, TRACKING, IMAGE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10167807 |
Archive Staff Only
 |
View Item |
