Hierarchical Object Detection applied to Fish Species

Abstract

Gathering information of aquatic life is often based on timeconsuming methods utilizing video feeds. It would be beneficial to capture more information cost-effectively from video feeds. Video based object detection has an ability to achieve this. Recent research has shown promising results with the use of YOLO for object detection of fish. As underwater conditions can be difficult and thus fish species are hard to discriminate. This study proposes a hierarchical structure-based YOLO Fish algorithm in both the classification and the dataset to gain valuable information. With the use of hierarchical classification and other techniques. YOLO Fish is a state-of-the-art object detector on Nordic fish species, with an mAP of 91.8%. The algorithm has an inference time of 26.4 ms, fast enough to run on real-time video on the high-end GPU Tesla V100.

Hierarchical Object Detection applied to Fish Species