WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929386883645440 |
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| author | Yao, Shanliang Guan, Runwei Wu, Zhaodong Ni, Yi Huang, Zile Liu, Ryan Wen Yue, Yong Ding, Weiping Lim, Eng Gee Seo, Hyungjoon Man, Ka Lok Ma, Jieming Zhu, Xiaohui Yue, Yutao |
| author_facet | Yao, Shanliang Guan, Runwei Wu, Zhaodong Ni, Yi Huang, Zile Liu, Ryan Wen Yue, Yong Ding, Weiping Lim, Eng Gee Seo, Hyungjoon Man, Ka Lok Ma, Jieming Zhu, Xiaohui Yue, Yutao |
| contents | Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivors rescue, environmental monitoring, hydrography mapping and waste cleaning. This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces. Equipped with a 4D radar and a monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather solutions for discerning object-related information, including color, shape, texture, range, velocity, azimuth, and elevation. Focusing on typical static and dynamic objects on water surfaces, we label the camera images and radar point clouds at pixel-level and point-level, respectively. In addition to basic perception tasks, such as object detection, instance segmentation and semantic segmentation, we also provide annotations for free-space segmentation and waterline segmentation. Leveraging the multi-task and multi-modal data, we conduct benchmark experiments on the uni-modality of radar and camera, as well as the fused modalities. Experimental results demonstrate that 4D radar-camera fusion can considerably improve the accuracy and robustness of perception on water surfaces, especially in adverse lighting and weather conditions. WaterScenes dataset is public on https://waterscenes.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_06505 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces Yao, Shanliang Guan, Runwei Wu, Zhaodong Ni, Yi Huang, Zile Liu, Ryan Wen Yue, Yong Ding, Weiping Lim, Eng Gee Seo, Hyungjoon Man, Ka Lok Ma, Jieming Zhu, Xiaohui Yue, Yutao Computer Vision and Pattern Recognition Robotics Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivors rescue, environmental monitoring, hydrography mapping and waste cleaning. This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces. Equipped with a 4D radar and a monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather solutions for discerning object-related information, including color, shape, texture, range, velocity, azimuth, and elevation. Focusing on typical static and dynamic objects on water surfaces, we label the camera images and radar point clouds at pixel-level and point-level, respectively. In addition to basic perception tasks, such as object detection, instance segmentation and semantic segmentation, we also provide annotations for free-space segmentation and waterline segmentation. Leveraging the multi-task and multi-modal data, we conduct benchmark experiments on the uni-modality of radar and camera, as well as the fused modalities. Experimental results demonstrate that 4D radar-camera fusion can considerably improve the accuracy and robustness of perception on water surfaces, especially in adverse lighting and weather conditions. WaterScenes dataset is public on https://waterscenes.github.io. |
| title | WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2307.06505 |