Rodoshi, Ahanaf HassanKhondaker, ArnishaFarhan, RafidRahman, Ninad AbdurAhsan, Syeda Sara Ummy2024-06-022024-06-0220222022-01ID 18101231ID 18101223ID 18101437http://hdl.handle.net/10361/23055Cataloged from PDF version of thesis.Includes bibliographical references (pages 42-43).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.Due to a number of reasons, marine ecosystems change with certain species of fish disappearing while novel species of fishes become a new staple within a given ecosystem, e.g., a lake, river, etc. Monitoring these changes in ecosystems as different species dwindle and swell in number is crucial for marine researchers, fishery owners, and fish species preservation programs. These increase and decrease in numbers indicate changes in environmental conditions that either favours a certain species or does not. In order to study these changes in conditions, it is imperative to firstly detect the changes in the population of species which is where we come in. The challenges for an underwater project range from water pressure, lack of sunlight, different orientations of fish, the motion of aquatic plants, riverbed structures, and the sheer diversity of shapes in different species. Machine learning and image processing technologies can be of significant importance in identifying such underwater fish species. In our research, we decided to use Convolutional Neural Networks (CNN), namely YOLOv4, to detect fish in input image frames. To classify the fish species, we will use a CNN network. The fusion of these networks is proposed in order to achieve a high level of classification accuracy of fish species from smallsized samples. In order to demonstrate the effectiveness of the model, we propose two datasets, namely BDIndigeneousFish and A-Large-Scale-Fish-Dataset is used, which contain a vast range of image data of several species from different habitats. The image data is fed into the Darknet, which identifies and detects the fish pixels in the image frame. Furthermore, these input images are then passed on to CNN for classification.43 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Fish detectionCNN model for classificationYOLOv4 for detectionArtificial Intelligence (AI)VGG-16DenseNetXceptionArtificial intelligenceNeural networks (Computer science)Thesis