DEEP CONVOLUTIONAL NETWORKS FOR UNDERWATER FISH LOCALIZATION AND SPECIES CLASSIFICATION

Live fish recognition is a difficult multi-class order task in the open sea. We propose a technique to perceive fish in an unlimited common habitat.In the proposed technique, VGG-16 with deep fish architecture is used to enhance the feature extraction what's more, to improve the exactness of the result.The proposed approach comprises of two fundamental stages; namely Fish Localization phase and Fish classification phase.The technique first detect the fish from the image by extracting feature map using VGG16 network. DeepFish architectureis used to categorize the Fish.Then, the proposed approach uses support vector machine and random forest classifier to differentiate between fish species. Experimental results obtained show that VGG16 with deepfish architecture using support vector machine attains a better accuracy of 99.47%.