Segmentation and classification of fish species using deep learning

Abstract

Fish Species Classification using deep learning surfaced as a formidable tool for automating and enhancing species identification in aquatic ecosystems. Moreover, leveraging Convolutional Neural Networks (CNNs), this approach presents us with an efficient and accurate way of identifying and categorizing fish under different species based on image data, providing us with a notable advancement for aquatic biodiversity surveillance and fisheries management. The switch towards deep learning addresses the constraints of traditional techniques such as manual labor and morphological analysis, which can be time consuming, require specialist knowledge and are inclined to human error. The primary objective of our study reviews recent advances in the field of deep learning, which focus on Convolutional Neural Networks and their application in classifying fish species as well as segmenting them through images of fishes overwater and maintaining a white background. We analyzed various approaches adopted in recent research, from CNN-based models like ResNet-50, Inception-v3, YOLOv11 etc. to innovative image preprocessing techniques, highlighting the evolution of methodologies from rudimentary to more sophisticated automated systems. We introduced a custom dataset of 7100 images which were captured during daylight as well as night time for better picture quality keeping in mind the reduction of computational intensity for real-time applications. In addition, through this research our aim is not only to refine the accuracy of fish species classification and segmentation but also to contribute significantly to the protection of marine ecosystems, aiding in the detection of rare species and assisting in sustainable fishing practices. To conclude, this study stands at the forefront of technological advancements in ecological conservation and offers in industrial use.