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The project report details a deep learning-based system for automated fish species recognition using underwater images, addressing the limitations of traditional manual identification methods. The study evaluates various Convolutional Neural Network architectures, with YOLOv5 demonstrating the highest accuracy and speed for real-time applications. Future research will focus on dataset improvement and the integration of additional data types for enhanced fish behavior analysis.

Author

suriya

Language

EN