Chinese White Dolphin Detection in the Wild
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings of the 3rd ACM International Conference on Multimedia in Asia, MMAsia '21 |
Publisher | Association for Computing Machinery |
ISBN (print) | 9781450386074 |
Publication status | Published - Dec 2021 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Title | 3rd ACM International Conference on Multimedia in Asia, MM Asia 2021 |
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Location | Hybrid |
Place | Australia |
City | Gold Coast |
Period | 1 - 3 December 2021 |
Link(s)
Abstract
For ecological protection of the ocean, biologists usually conduct line-transect vessel surveys to measure sea species' population density within their habitat (such as dolphins). However, sea species observation via vessel surveys consumes a lot of manpower resources and is more challenging compared to observing common objects, due to the scarcity of the object in the wild, tiny-size of the objects, and similar-sized distracter objects (e.g., floating trash). To reduce the human experts' workload and improve the observation accuracy, in this paper, we develop a practical system to detect Chinese White Dolphins in the wild automatically. First, we construct a dataset named Dolphin-14k with more than 2.6k dolphin instances. To improve the dataset annotation efficiency caused by the rarity of dolphins, we design an interactive dolphin box annotation strategy to annotate sparse dolphin instances in long videos efficiently. Second, we compare the performance and efficiency of three off-the-shelf object detection algorithms, including Faster-RCNN, FCOS, and YoloV5, on the Dolphin-14k dataset and pick YoloV5 as the detector, where a new category (Distracter) is added to the model training to reject the false positives. Finally, we incorporate the dolphin detector into a system prototype, which detects dolphins in video frames at 100.99 FPS per GPU with high accuracy (i.e., 90.95 [email protected]).
Research Area(s)
- datasets, detection system, dolphin detection, neural networks
Citation Format(s)
Chinese White Dolphin Detection in the Wild. / Zhang, Hao; Zhang, Qi; Nguyen, Phuong Anh et al.
Proceedings of the 3rd ACM International Conference on Multimedia in Asia, MMAsia '21. Association for Computing Machinery, 2021. 44 (ACM International Conference Proceeding Series).
Proceedings of the 3rd ACM International Conference on Multimedia in Asia, MMAsia '21. Association for Computing Machinery, 2021. 44 (ACM International Conference Proceeding Series).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review