| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560654 |
| ISSN: | 1405-5546 |
| Autores: | Jany Arman, Rafsun1 Hossain, Monowar1 Hossain, Sabir2 |
| Instituciones: | 1Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Computer Science and Engineering, Bangladés 2Chittagong University of Engineering and Technology, Computer Science and Engineering, Chittagong Division. Bangladés |
| Año: | 2022 |
| Periodo: | Ene-Mar |
| Volumen: | 26 |
| Número: | 1 |
| Paginación: | 303-310 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Classification of fishes becomes important after the advancement of machine learning. As fishes play a vital role in the economy of Bangladesh, a proper monitoring system will maximize the cultivation. It will also contribute to the overall economy. Therefore, here introduce a system that can detect the fishes and compare various methods with explanations to understand the selected methods. This paper have considered 5 categories of local fishes of Bangladesh in the dataset. The technique consists of preprocessing with segmentation, feature descriptor, and ensembles to produce the final result. U2-net is used in the preprocessing layer to obtain two types of features namely shaped images and colored images with removed backgrounds. To get the features, we have used a histogram of oriented gradient (HOG) and an ensemble layer is used for classification purposes. Experimental results illustrate the accuracy of 99.77% for the first ensemble and 100% for the second ensemble layer on our dataset of 2678 fishes of 5 distinguishing classes. Various layers were used to compare the predicted results using different performance metrics. |
| Keyword: | U2-net, Hog, Knn, SVM, Logistic regression, Decision tree, Fish classification, Segmentation, Salient object detection |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
