Development of an ATrUNet Architecture for Image Segmentation of Oncorhynchus mykiss in the Peruvian Highlands

Image segmentation enables accurate object identification, a key requirement in computer vision applications. In aquaculture, this technology is essential for monitoring and management of species such as Oncorhynchus mykiss. In the Peruvian highlands, where trout farming is a vital economic activity, robust computational models are needed to automate the estimation of fish size and weight and to optimize the sustainability of production systems. This research proposes ATrUNet, a U-Net-based architecture optimized for accurate segmentation of Oncorhynchus mykiss. ATrUNet improves the information flow between the encoding and decoding layers by incorporating convolutional layers, batch normalization, and activation functions. A dataset of 1166 images was constructed, processed using LabelMe with JSON annotations, and converted into binary masks. Evaluation was conducted using loss, accuracy, and IoU. As a result, ATrUNet showed higher performance than U-Net, achieving a 27.55% reduction in loss, a 0.40% increase in accuracy (0.992), and improvements in overlap metrics such as IoU and GIoU by 2.70% and 8.75%, respectively. Future work includes expanding dataset diversity, exploring instance segmentation, and optimizing for embedded deployment. This research contributes to the application of computer vision to aquaculture, with the possibility of extending it to different species and various research contexts.