A real‐world dataset and data simulation algorithm for automated fish species identification. (18th March 2021) - Allken, Vaneeda; Rosen, Shale; Handegard, Nils Olav; Malde, Ketil
Abstract: Developing high‐performing machine learning algorithms requires large amounts of annotated data. Manual annotation of data is labour‐intensive, and the cost and effort needed are an important obstacle to the development and deployment of automated analysis. In a previous work, we have shown that deep learning classifiers can successfully be trained on synthetic images and annotations. Here, we provide a curated set of fish image data and backgrounds, the necessary software tools to generate synthetic images and annotations, and annotated real datasets to test classifier performance. The dataset is constructed from images collected using the Deep Vision system during two surveys from 2017 and 2018 that targeted economically important pelagic species in the Northeast Atlantic Ocean. We annotated a total of 1, 879 images, randomly selected across trawl stations from both surveys, comprising 482 images of blue whiting, 456 images of Atlantic herring, 341 images of Atlantic mackerel, 335 images of mesopelagic fishes and 265 images containing a mixture of the four categories. Abstract : Developing high‐performing machine learning algorithms requires large amounts of annotated data for training. Manual annotation of data is labour‐intensive, and the cost/ effort needed is an important obstacle to the development and deployment of automated analysis. Here, we provide a curated set of 1, 879 images with 4, 328 individual annotations of four species collected inside a sampling trawl during surveys of economically important pelagic species in the Northeast Atlantic Ocean. Code for generating synthetic images for training is also provided.