Marine life through You Only Look Once's perspective
This work addresses the need for sustainable resource management by providing a tool for the Norwegian government to monitor fish populations, but it is incremental as it applies an existing object detection method to a new dataset.
The paper tackled the problem of detecting fish in camera images from a submerged data station in Norway to monitor maritime wildlife, achieving a mean average precision (mAP) of approximately 0.88 on a dataset of 99,961 images.
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.