Kraken: Open-Source Computer Vision for Underwater Fish Detection and Measurement - Gohl, Pedro; Rocha, Herbert; Lobo, Felipe; Balico, Leandro (Anais do Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware); 2025: Anais do XXII Congresso Latino-Americano de Software Livre e Tecnologias AbertasDO - 10.5753/latinoware.2025.16326, 2025)
Brazil’s extensive river networks, particularly in the Amazon Basin, are characterized by high turbidity and low underwater visibility, presenting significant challenges for aquatic monitoring and fish farming operations. This study introduces Kraken, an open-source computational system designed for automated fish detection, counting, and size estimation in turbid freshwater environments. Kraken integrates state-of-the-art computer vision frameworks—including YOLO, OpenCV, and TensorFlow—with open hardware platforms such as Arduino and Raspberry Pi, enabling cost-effective and scalable deployment. The system employs a pre-trained Inception convolutional neural network for robust fish classification and utilizes image preprocessing techniques to enhance detection accuracy under challenging conditions. Experimental evaluation demonstrates Kraken’s reliability, achieving an average size estimation at a fixed distance of 30 cm and a classification accuracy of up to 90%. By automating fish counting and measurement, Kraken reduces manual labor and operational costs, while its open-source architecture fosters collaborative innovation and adaptability. The results confirm Kraken’s effectiveness for real-world aquatic monitoring, highlighting its potential to advance research and practical applications in fish farming and ecosystem management.