An Intelligent Deep Learning Enabled Marine Fish Species Detection and Classification Model

Abstract

In recent times, marine fish species recognition becomes an important research area to protect the ocean environment. It is a tough and time-consuming operation to manually detect marine fish species on the ocean floor. Depending on the situation, extensive sample efforts may be required. These efforts might be harmful to the marine ecosystem. Automated classification methods are capable of properly classifying these fish on a consistent basis. An increasing number of people are becoming interested in utilizing electronic monitoring and reporting with artificial intelligence for the aim of fish identification and enhancing present techniques. It is becoming more usual to use video and pictures of fish (either underwater or on ships) in fishing operations. These techniques are operational, transportable, and non-invasive, and they provide high-quality pictures at a lower cost than traditional approaches. Automated image processing techniques such as Deep Learning (DL) and Machine Learning (ML) are now available, and they may be customized to perform efficient fish species identification and segmentation. In this aspect, this paper presents an Intelligent DL based Marine Fish Species Classification (IDL-MFSC) technique. The proposed IDL-MFSC technique involves three major processes such as pre-processing, fish detection and fish classification. Primarily, Weiner filtering-based noise removal process takes place as a pre-processing step. In addition, Mask R-CNN (Mask Region Based Convolutional Neural Networks) with Residual Network as a backbone network is used for fish detection. Moreover, Optimal Deep Kernel Extreme Learning Machine (ODKELM) based classification method is employed for determining the class labels of the marine fish species in which the parameter tuning of the DKELM model takes place using Water Wave Optimization (WWO) technique. The performance of the proposed method is tested using an openly accessible Fish4Knowledge dataset. The experimental result highlights the supremacy of the IDL-MFSC technique over the recent techniques with respect to various measures.