Fish Classification Using Saliency Detection Depending on Shape and Texture

Md. Rafsun Jany Arman, Md. Monowar Hossain, Md. Sabir Hossain


Classification of fishes becomes important after the advancement of machine learning. As fishes play a vital role in the economy of Bangladesh, a proper monitoring system will maximize the cultivation. It will also contribute to the overall economy. Therefore, here introduce a system that can detect the fishes and compare various methods with explanations to understand the selected methods. This paper have considered 5 categories of local fishes of Bangladesh in the dataset. The technique consists of preprocessing with segmentation, feature descriptor, and ensembles to produce the final result. U2 -net is used in the preprocessing layer to obtain two types of features namely shaped images and colored images with removed backgrounds. To get the features, we have used a histogram of oriented gradient (HOG) and an ensemble layer is used for classification purposes. Experimental results illustrate the accuracy of 99.77% for the first ensemble and 100% for the second ensemble layer on our dataset of 2678 fishes of 5 distinguishing classes. Various layers were used to compare the predicted results using different performance metrics.


U 2 -net, hog, knn, svm, logistic regression, decision tree, fish classification, segmentation, salient object detection

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