Abstract
About fifty percent of the world relies on seafood as main protein source. Due to this, the illegal and uncultured fishery activities are proving to be a threat to marine life. The paper discusses a novel technique that automatically detects and classifies various species of fishes such as dolphins, sharks etc. to help and protect endangered species. Images captured through boat-cameras have various hindrances such as fluctuating degrees of luminous intensity and opacity. The system implemented, aims at helping investigators and nature conservationists, to analyze images of fishes captured by boat-cameras, detect and classify them into species of fishes based on their features. The system adapts to the variations of illumination, brightness etc. for the detection process. The system incorporates a three phase methodology. The first phase is augmentation. This phase involves using data augmentation techniques on real time images dataset captured by boat-cameras and is passed to the detection module. The second phase is the detection. This phase involves detecting fishes in the image by searching for regions in the image having high probability of fish containment. The third phase is the classification of the detected fish into its species. This step involves the segmented image of fishes to be passed to the classifier model which specifies to which species the detected fish belongs to. CNN (Convolutional neural network) is used at the detection and classification phase, with different architectures, to extract and analyze features. The system provides confidence quotients on each image, expressed on a 0–1 scale, indicating the likelihood of the image belonging to each of the following eight categories ALB, BET, YFT, LAG, DOL, Shark, Other and None. The system provides detection and classification with an accuracy of 90% and 92% respectively.
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Rekha, B.S., Srinivasan, G.N., Reddy, S.K., Kakwani, D., Bhattad, N. (2020). Fish Detection and Classification Using Convolutional Neural Networks. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_128
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