Crosspooled FishNet: transfer learning based fish species classification model
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Crosspooled FishNet: transfer learning based fish species classification model Monika Mathur1 · Diksha Vasudev1 · Sonalika Sahoo1 · Divanshi Jain1 · Nidhi Goel1 Received: 19 July 2019 / Revised: 7 July 2020 / Accepted: 16 July 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Fish species classifiction is an important task for biologists and marine ecologists to frequently estimate the relative abundance of fish species in their natural habitats and monitor changes in their populations. Traditional methods used for fish species classifiction were laboriuos, time consuming and expensive. So, there is need for an automated system that can not only detect and track but also categorize fish as well as other aquatic species in underwater imagery, minimizing the manual interference. Absorption and scattering of light in deep sea environment leads to low resolution images making fish species recognition and classification a challenging task. Further, performance of traditional computer vision techniques tends to degrade in underwater conditions due to the presence of high background clutter and highly indistinct features of marine species. For such classification problems, Artificial Neural Networks (ANN) or deep neural network are being increasingly employed for improved performance. But the limited dataset of fish images makes it difficult to train such networks as they require huge datasets for training. Thus to reduce the requirement for a huge amount of training data, an algorithm using cross convolutional layer pooling on a pre-trained Convolutional Neural Networks (CNN) is proposed. The present paper focuses on the development of automatic system for classification, which can detect and classify fish from underwater images captured through videos. Thorough analysis on image dataset of 27,370 fish images gives a validation accuracy of 98.03%. The proposed method will be an efficient replacement to strenuous and time consuming method of manual recognition by marine experts and thus be advantageous for monitoring fish biodiversity in their natural habitats. Keywords Underwater images · Convolution neural network · Fish species · ResNet · Cross convolutional layer pooling
1 Introduction Object recognition and classification is one of the most prominent topic of reseach for underwater environment [36, 37]. But underwater imagery is a strenuous task, which Nidhi Goel
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Multimedia Tools and Applications
requires the high-resolution camera equipment to overcome scattering and absorption of light in ocean water [26]. Marine biologist and ocean researchers are interested in observing the health and status of fish aggregation targeted by fisheries, fish population of the particular species and information about the corresponding abundance of fish and their distribution. Climatic changes due to global warming, weaken the ability of the ocean and coasts to provide the critical ecosystem for marin
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