FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters

• Published in: 2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 10933 - 10942
• Main Authors: Cheng, Yuwei, Zhu, Jiannan, Jiang, Mengxin, Fu, Jie, Pang, Changsong, Wang, Peidong, Sankaran, Kris, Onabola, Olawale, Liu, Yimin, Liu, Dianbo, Bengio, Yoshua
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2021
• Subjects: Annotations; Datasets and evaluation; Millimeter wave radar; Object detection; Radar detection; Radar imaging; Sea surface; Surface cleaning
• ISSN: 2380-7504
• DOI: 10.1109/ICCV48922.2021.01077

Marine debris is severely threatening the marine lives and causing sustained pollution to the whole ecosystem. To prevent the wastes from getting into the ocean, it is helpful to clean up the floating wastes in inland waters using the autonomous cleaning devices like unmanned surface vehicles. The cleaning efficiency relies on a high-accurate and robust object detection system. However, the small size of the target, the strong light reflection over water surface, and the reflection of other objects on bank-side all bring challenges to the vision-based object detection system. To promote the practical application for autonomous floating wastes cleaning, we present FloW † , the first dataset for floating waste detection in inland water areas. The dataset consists of an image sub-dataset FloW-Img and a multimodal sub-dataset FloW-RI which contains synchronized millimeter wave radar data and images. Accurate annotations for images and radar data are provided, supporting floating waste detection strategies based on image, radar data, and the fusion of two sensors. We perform several baseline experiments on our dataset, including vision-based and radar-based detection methods. The results show that, the detection accuracy is relatively low and floating waste detection still remains a challenging task.