Deep Blue Discoveries: Advancements in Fish Detection and Species Taxonomy Using Deep Learning in the Subaquatic Realm

Abstract

Abstract: The Deep Learning Based Underwater Fish Detection and Species Classification project employs deep learning methodologies to accurately detect and classify underwater fish species using a dataset consisting of diverse fish images captured from different underwater locations. The project utilizes a Region-based Convolutional Neural Network (R-CNN) to extract image features, ensuring precise species identification. The model's performance is evaluated using accuracy metrics to assess its effectiveness in fish detection and species classification. This project holds practical implications for the field of fisheries management and conservation by enabling accurate identification and classification of fish species in underwater environments. Moreover, the project's outcomes have the potential to find applications in marine biology and ecology, serving as a valuable tool for studying fish behavior and ecological dynamics within their natural habitats. The project's successful implementation underscores the transformative potential of deep learning techniques in the domain of computer vision, specifically in tasks such as image classification, segmentation, and object detection. In conclusion, the Deep Learning Based Underwater Fish Detection and Species Classification project employs advanced deep learning techniques to accurately identify and categorize various fish species in underwater environments. The project's diverse dataset, encompassing fish images captured from different underwater locations, enhances its significance for the fields of fisheries management and conservation. The project's outcomes have the potential to significantly contribute to our understanding of underwater ecosystems and support endeavors aimed at their protection and preservation