Hierarchical classification, counting and length measurement of fish using a stacking model approach Article Swipe
YOU?
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· 2020
· Open Access
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· DOI: https://doi.org/10.58895/ksp/1000124383-28
· OA: W4396683321
In this paper, the development of a hierarchical fish classification framework is presented. The conventional data collection technique for the commercial fish stock assessment is a labour intensive and time consuming procedure. The purpose of this project is to develop a framework that classifies fish species on two level semantic hierarchy label, to count the number of fishes and to measure the length of four different fish species using a small dataset. In stage 1 of the framework, the YOLOv3 convolutional neural network is used to accomplish level one semantic hierarchy label, to count the number of fishes and to measure the length of the detected fish. In stage 2, the features from the images are extracted using the VGG16 convolutional neural network. In stage 3, the stacked generalization technique is implemented to reduce the generalization error and to accomplish level two semantic hierarchy label. The classification accuracy of the stack model is 94%. The root mean square error of the fish length measurement is 1.23 cm. The accuracy in counting the number of fish depends on the detection accuracy of the stage 1 model and the classification accuracy of the stack models. Further, the results can be improved by increasing the size and diversity of the dataset.
