Underwater Fish Detection and Classification using Deep Learning

Abstract

The researchers face a difficult problem in detecting and identifying underwater fish species. Marine researchers and ecologists must evaluate the comparative profusion of fish species in their environments on a regular basis and track population trends. Researchers have presented a number of underwater computer vision, machine learning-based automatic systems for fish detection and classification. However, due of the changing undersea environment, it is extremely challenging to find the ideal system for detecting and classifying fish. Because light has such a strong influence in the aqueous medium, conducting research in this environment is difficult. The MobileNet model is utilised to detect and recognise the fish breed in the proposed work. The dataset is preprocessed before the model is implemented in order to obtain appropriate performance metrics. The work is based on the Kaggle dataset, which has nine different fish breeds in total. With a 99.74 percent accuracy, the model can detect and recognise nine different breeds. In comparison to other state of art methods, the model exhibits promising results.