Improving the classification accuracy of fishes and invertebrates using residual convolutional neural networks

Abstract

Abstract The visibility of fishes and invertebrates is highly impacted by the complexity of the environment. Images acquired in underwater environments suffer from blurriness and low contrast. This results in a low classification accuracy. To address this problem, this study uses a pre-trained Resnet50 neural network as the feature extractor, which avoids over-fitting and accuracy saturation while realizing improved feature extraction capabilities. It also proposes an enhancement of the error-minimized random vector functional link (EEMRVFL) neural network, which is used as the classifier in the convolutional neural network (CNN) model instead of the original softmax classifier. EEMRVFL reduces the maximum residual error in each incremental process. The selected hidden nodes are added to the network, which improves the compactness of its structure. The proposed residual CNNs model exhibits improved classification accuracy for underwater image classification compared to existing methods. This is demonstrated experimentally on available datasets such as URPC, LifeCLEF 2015, and Fish4Knowledge with accuracy rates reaching 99.68%, 97.34%, and 99.77%, respectively.