Ship Detection in Maritime Scenes under Adverse Weather Conditions

被引:0
|
作者
Zhang, Qiuyu [1 ]
Wang, Lipeng [1 ]
Meng, Hao [1 ]
Zhang, Zhi [1 ]
Yang, Chunsheng [2 ]
机构
[1] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150001, Peoples R China
[2] Natl Res Council Canada, Ottawa, ON K1A 0R6, Canada
基金
中国国家自然科学基金;
关键词
object detection; LiDAR; autonomous driving; data simulation;
D O I
10.3390/rs16091567
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Point cloud-based detection focuses on land traffic, rarely marine, facing issues with ships: it struggles in bad weather due to reliance on adverse weather data and fails to detect ships effectively due to overlooking size and appearance differences. Addressing the above challenges, our work introduces point cloud data of marine scenarios under realistically simulated adverse weather conditions and a dedicated Ship Detector tailored for marine environments. To adapt to various maritime weather conditions, we simulate realistic rain and fog in collected marine scene point cloud data. Additionally, addressing the issue of losing geometric and height information during feature extraction for large objects, we propose a Ship Detector. It employs a dual-branch sparse convolution layer for extracting multi-scale 3D feature maps, effectively minimizing height information loss. Additionally, a multi-scale 2D convolution module is utilized, which encodes and decodes feature maps and directly employs 3D feature maps for target prediction. To reduce dependency on existing data and enhance model robustness, our training dataset includes simulated point cloud data representing adverse weather conditions. In maritime point cloud ship detection, our Ship Detector, compared to adjusted small object detectors, demonstrates the best performance.
引用
收藏
页数:21
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