Chinese White Dolphin Detection in the Wild

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/blurryness 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. And 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 mAP@0.5).

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