Hierarchical Fish Species Detection in Real-Time Video Using YOLO

Master's thesis in Information- and communication technology (IKT590)Information gathering of aquatic life is often based on time consuming methods with a foundation in video feeds. It would be beneficial to capture more information in a cost effective manner from video feeds, and video object detection has an opportunity to achieve this. Recent research has shown promising results with the use of YOLO for object detection of fish. As under-water conditions can be difficult and fish species hard to discriminate, we propose the use of a hierarchical structures in both the classification and the dataset to gain valuable information. With the use of hierarchical classification and other techniques we present YOLO Fish. YOLO Fish is a state of the art object detector on nordic fish species, with an mAP of 91.8%. For a more stable video, YOLO Fish can be used with the object tracking algorithm SORT. This results in a complete fish detector for real-time video

Hierarchical Object Detection applied to Fish Species

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 SpeciespublishedVersio

Hierarchical Fish Species Detection in Real-Time Video Using YOLO

Information gathering of aquatic life is often based on time consuming methods with a foundation in video feeds. It would be beneficial to capture more information in a cost effective manner from video feeds, and video object detection has an opportunity to achieve this. Recent research has shown promising results with the use of YOLO for object detection of fish. As under-water conditions can be difficult and fish species hard to discriminate, we propose the use of a hierarchical structures in both the classification and the dataset to gain valuable information. With the use of hierarchical classification and other techniques we present YOLO Fish. YOLO Fish is a state of the art object detector on nordic fish species, with an mAP of 91.8%. For a more stable video, YOLO Fish can be used with the object tracking algorithm SORT. This results in a complete fish detector for real-time video

Hierarchical Object Detection applied to Fish Species: Hierarchical Object Detection of Fish Species

Gathering information of aquatic life is often based on timeconsumingmethods utilizing video feeds. It would be beneficialto 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 ofYOLO for object detection of fish. As underwater conditionscan be difficult and thus fish species are hard to discriminate.This study proposes a hierarchical structure-based YOLO Fishalgorithm in both the classification and the dataset to gainvaluable information. With the use of hierarchical classificationand other techniques. YOLO Fish is a state-of-the-art objectdetector on Nordic fish species, with an mAP of 91.8%. Thealgorithm has an inference time of 26.4 ms, fast enough torun on real-time video on the high-end GPU Tesla V100.Gathering information of aquatic life is often based on timeconsumingmethods utilizing video feeds. It would be beneficialto 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 ofYOLO for object detection of fish. As underwater conditionscan be difficult and thus fish species are hard to discriminate.This study proposes a hierarchical structure-based YOLO Fishalgorithm in both the classification and the dataset to gainvaluable information. With the use of hierarchical classificationand other techniques. YOLO Fish is a state-of-the-art objectdetector on Nordic fish species, with an mAP of 91.8%. Thealgorithm has an inference time of 26.4 ms, fast enough torun on real-time video on the high-end GPU Tesla V100