Authors: Akash Pokharkar, Niranjan Deshmukh , Manmath Biradar
DOI Link: https://doi.org/10.22214/ijraset.2024.62719
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This research explores the application of deep learning techniques for fish species detection in underwater environments. convolutional neural networks (CNNs) trained on extensive datasets, the study aims to enhance the accuracy and efficiency of species identification. The proposed model demon- strates promising results in differentiating diverse fish species, contributing to advancements in aquatic ecology monitoring and biodiversity conservation. The integration of deep learning in fish species detection holds potential for improving our understanding of underwater ecosystems and supporting sustainable fisheries management. The relative abundance of fish pieces in their habitats on a regular basis and keeping an eye on population fluctuations, this are a crucial task for marine scientists and conservationists diverse automatic computer based fish sample methods have been demonstrated in underwater photos and videos as alternatives to time consuming hand sampling there isn’t however a perfect method for automatically detecting fish and classifying the species this is mostly due to the difficulties in producing clear underwater images and videos which include environmental fluctuations in lightning fish camouflage Dynamic backdrops murky water low resolution shape deformations of moving fish.
I. INTRODUCTION
Dive into the future of aquatic research with fish species detection powered by deep learning. fish species identification and detection through the lens of deep learning. In this rapidly evolving field, cutting-edge technologies harness the power of artificial intelligence to revolutionize our ability to discern and classify diverse aquatic life. By delving into the intricacies of deep learning algorithms, we embark on a journey to enhance our understanding of fish biodiversity, contributing to both conservation efforts and the sustainable management of aquatic ecosystems.
We explore the intersection of technology and marine biology, unlocking new possibilities for accurate, efficient, and non-intrusive methods of fish species identification.
Deep convolutional neural network (CNN) models through ensemble learning. This approach aims to significantly enhance diagnostic accuracy and reliability, facilitating early intervention and treatment. Embark on an exploration at the intersection of marine biology and cutting-edge technology as we delve into fish species detection using Convolutional Neural Networks (CNNs). CNNs, renowned for their prowess in image analysis, provide a powerful tool for discerning intricate patterns and features within underwater imagery.
The transformative impact of artificial intelligence, particularly deep learning, on the field of aquatic research. It emphasizes the revolutionary potential of these technologies in discerning and classifying diverse aquatic life. The primary objective is to improve our understanding of fish biodiversity, with a specific focus on fish species identification. This is seen as a crucial contribution to conservation efforts and the sustainable management of aquatic ecosystems. CNNs are recognized for their effectiveness in image analysis, making them a suitable choice for the complex task of fish species identification within underwater imagery.
Ensemble learning combines the predictions of multiple models to enhance diagnostic accuracy and reliability. The goal is to improve the efficiency of fish species identification, enabling early intervention and treatment in the context of aquatic research. The narrative invites readers to explore the intersection of marine biology and cutting-edge technology. It specifically emphasizes how CNNs, known for their ability to analyze images, can be a powerful tool for detecting intricate patterns and features within underwater imagery, facilitating accurate fish species identification.
II. METHODS
A. Data Augmentation
With this technique we generate additional training data by applying various transformations such as rotations, shifts, and flips. This process enhances the diversity of the training dataset, allowing the model to generalize better by being exposed to a wider array of scenarios. By augmenting the data in this manner, the model becomes more robust and capable of performing well on new, unseen data, ultimately improving its overall performance and reliability.
B. Feature Extraction
In the realm of fish species detection, feature extraction entails the process of isolating and analyzing distinctive traits from original data, such as images or sensor readings, which serve as indicators for differentiating between various fish species. These traits encompass a range of characteristics including color patterns, texture, shape, and other morpholog- ical attributes. Commonly employed techniques involve edge detection, analysis of color histograms, and texture assessment, all aimed at distilling pertinent features. The objective is to condense the data’s complexity while retaining its essential information, thereby facilitating subsequent classification or identification tasks through machine learning algorithms. By effectively capturing the unique traits specific to each species, feature extraction plays a pivotal role in automating fish species detection endeavors, thereby contributing to fisheries management, biodiversity evaluation, and ecological research.
C. Deep learning models
In this section, we detail the architectures and configura- tions of the models employed for cross-domain aspect-based sentiment analysis. Two distinctive models, a Long Short-Term Memory (LSTM) network and a Gated Recurrent Unit (GRU), were implemented to capture nuanced sentiment expressions across diverse domains.
III. RESULTS
A. Performance Metrics
For assessing the performance of the fish species detection model, several evaluation metrics were employed.
B. Comparative Analysis
In comparison to established baseline models, our frame- work exhibited significant improvements in both accuracy and F1-score. The comparative analysis underscores the effective- ness of our proposed methodology in enhancing sentiment analysis performance across various domains.
Fish species detection using deep learning represents a promising avenue for revolutionizing the identification process of fish species from images. By harnessing the power of advanced neural network architectures, these systems exhibit the potential to achieve unprecedented levels of accuracy and generalization across diverse datasets. Through the intricate layers of convolutional neural networks (CNNs) and other sophisticated techniques, these models can discern intricate patterns and features from images, which helps us for precise and accurate classification of fish species. The ability to learn from vast amounts of labeled data enables these systems to recognize subtle differences among species, even in instances where human interpretation might falter. Moreover, the scal- ability of deep learning frameworks empowers these systems to handle large datasets efficiently, facilitating the analysis of extensive collections of fish images with remarkable speed and accuracy.
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Copyright © 2024 Akash Pokharkar, Niranjan Deshmukh , Manmath Biradar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET62719
Publish Date : 2024-05-25
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here