YOLOv8 based real-time underwater fish monitoring framework: detection, tracking, and counting

YOLO (You Only Look Once) is one of the most popular computer vision algorithms. Computer vision has revolutionised the field of moving object detection in real time with its ability to analyse and understand visual content much like a human. This paper presents a comprehensive framework for fish detection, bounding box-based tracking, and counting in underwater environments using the YOLOv8 deep learning architecture. Accurately and efficiently identifying, tracking, and counting fish plays an important role in aquatic research, conservation efforts, and fishery management. The proposed system uses a pre-trained YOLOv8 model which is fine tuned using a large annotated dataset of underwater fish images. The model is improved using transfer learning to learn features specific to fish detection in water. Real-time underwater fish detection is performed on underwater video streams using a fine-tuned YOLOv8 model. The high speed and accuracy of YOLOv8 enables efficient localisation of fish instances at every frame. The analysis of such data enables accurate fish counts and facilitates effective monitoring and assessment of fish populations in water bodies. The true positive rate of 0.91 and accuracy of 92% indicated that the system successfully identified a significant proportion of fish instances present in the images.