Seabream Freshness Classification Using Vision Transformers.

Many different cultures and countries have fish as a central piece in their diet, particularly in coastal countries such as Portugal, with the fishery and aquaculture sectors playing an increasingly important role in the provision of food and nutrition. As a consequence, fish-freshness evaluation is very important, although so far it has relied on human judgement, which may not be the most reliable at times. This paper proposes an automated non-invasive system for fish-freshness classification, which takes fish images as input, as well as a seabream fish image dataset. The dataset will be made publicly available for academic and scientific purposes with the publication of this paper. The dataset includes metadata, such as manually generated segmentation masks corresponding to the fish eye and body regions, as well as the time since capture. For fish-freshness classification four freshness levels are considered: very-fresh, fresh, not-fresh and spoiled. The proposed system starts with an image segmentation stage, with the goal of automatically segmenting the fish eye region, followed by freshness classification based on the eye characteristics. The system employs transformers, for the first time in fish-freshness classification, both in the segmentation process with the Segformer and in feature extraction and freshness classification, using the Vision Transformer (ViT). Encouraging results have been obtained, with the automatic fish eye region segmentation reaching a detection rate of 98.77%, an accuracy of 96.28% and a value of the Intersection over Union (IoU) metric of 85.7%. The adopted ViT classification model, using a 5-fold cross-validation strategy, achieved a final classification accuracy of 80.8% and an F1 score of 81.0%, despite the relatively small dataset available for training purposes.