Under Water Fish Detection Using Faster RCNN and Yolov5

Project Code :TCMAPY1120

Objective

The primary objective of this project is to develop and refine a highly accurate and efficient system for detecting fish in underwater environments, using advanced object detection models, YOLOv5 and Faster R-CNN. By harnessing the capabilities of these cutting-edge convolutional neural networks, the project aims to address the challenges associated with underwater fish detection, such as varying light conditions, the presence of obstructions, and the need for real-time processing. This endeavor seeks to compare the performance of YOLOv5 and Faster R-CNN in terms of detection speed, accuracy, and reliability under diverse conditions, with the goal of identifying the most suitable model or combination of models for real-world applications. Ultimately, this project intends to contribute to the fields of marine biology, conservation efforts, and sustainable fishing practices by providing a robust tool for monitoring aquatic life, thus enabling better management of marine resources and preservation of aquatic ecosystems.

Abstract

This study presents an innovative approach to fish target detection by leveraging the advanced capabilities of YOLOv5 and Faster R-CNN, two of the leading convolutional neural network models designed for real-time object detection. With the aim of enhancing the accuracy and efficiency of underwater fish detection in diverse and complex aquatic environments, this research compares the performance of YOLOv5 and Faster R-CNN in terms of detection speed, accuracy, and reliability. Utilizing a comprehensive dataset of underwater images featuring various fish species in different settings, the study meticulously evaluates each model's ability to accurately identify and locate fish targets amidst background noise and varying visibility conditions. The findings demonstrate that YOLOv5, known for its speed and lightweight architecture, excels in scenarios requiring real-time detection, while Faster R-CNN, with its emphasis on detection accuracy through region proposal mechanisms, shows superior precision in complex image contexts. This comparative analysis not only sheds light on the strengths and weaknesses of each model in fish detection tasks but also provides valuable insights for the development of more effective and adaptive aquatic life monitoring systems. Through this exploration, the research contributes to the advancement of marine biology studies, sustainable fishing practices, and the preservation of aquatic ecosystems by offering a robust tool for accurate and efficient fish detection.

Keywords: YOLOv5, Faster R-CNN, Real-time object detection, Underwater image analysis, Aquatic environments

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.