Marine life through You Only Look Once's perspective

TL;DR

This paper applies YOLOv3 to detect fish in underwater images from Norway, creating a large dataset and analyzing detection performance to aid marine wildlife monitoring and conservation efforts.

Contribution

It introduces a large underwater fish dataset and provides an in-depth analysis of YOLOv3's intermediate detection results for marine life monitoring.

Findings

Achieved a mean Average Precision (mAP) of approximately 0.88 on the dataset.

Provided insights into YOLOv3's detection process in underwater environments.

Demonstrated the potential of real-time object detection for marine wildlife conservation.

Abstract

With the rise of focus on man made changes to our planet and wildlife therein, more and more emphasis is put on sustainable and responsible gathering of resources. In an effort to preserve maritime wildlife the Norwegian government has decided that it is necessary to create an overview over the presence and abundance of various species of wildlife in the Norwegian fjords and oceans. In this paper we apply and analyze an object detection scheme that detects fish in camera images. The data is sampled from a submerged data station at Fulehuk in Norway. We implement You Only Look Once (YOLO) version 3 and create a dataset consisting of 99,961 images with a mAP of $\sim 0.88$. We also investigate intermediate results within YOLO, gaining insight into how it performs object detection.