A multi-class framework for fish species classification using deep learning technique

Abstract

Fish species recognition is essential for ecological studies, fishery management, and marine biology. Accurate detection and categorization are critical for preserving biodiversity, allowing scientists to track species distribution, identify invasive species, and analyze the effects of environmental changes. The fish sector is essential to any country's food and agriculture. Identification of species by the morphology process is both inaccurate and costly. However, the manual process of measuring important details like species identification, length, and quantity is difficult to capture, which shows the need for automation. The merging of automated systems and artificial intelligence has revolutionized this industry. Recent advancements in image detection systems based on machine learning and deep learning have been explored across various domains. Yet, applying state-of-the-art deep model Convolutional Neural Networks (CNNs) to identify the fish species’ complexity of season and location, and limited public datasets pose a challenge for the detection. Machine learning and deep learning use artificial neural networks to simulate how humans think and learn, efficiently automating similar monitoring applications such as species identification on land and in water. You Only Look Once (YOLO) is a state-of-the-art method for object detection based on deep learning. The goal of this study is to develop a deep learning system for recognizing fish species using the YOLO paradigm. The Fish-Pak dataset, which includes information on tropical fish farming in Pakistan, consists of 915 images against 6 targeted classes, freely available at the Mendeley data source. To ensure the suggested YOLO architecture's improved performance on the Fish-Pak data collection, we will conduct an experimental comparison with other versions of YOLO v3 and V4. The total accuracy of fish species identification using the proposed methods is 99%, with an mAP of 99.65%, top performance results as compared to existing literature.