Szczegóły publikacji
Opis bibliograficzny
Improved binary classification of underwater images using a modified ResNet-18 model / Mehr Un NISA, Mikołaj LESZCZUK, Dawid JUSZKA, Yi Zhang // Electronics [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2079-9292. — 2025 — vol. 14 iss. 15 art. no. 2954, s. 1–24. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 21–24, Abstr. — Publikacja dostępna online od: 2025-07-24
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 161564 |
|---|---|
| Data dodania do BaDAP | 2025-08-22 |
| Tekst źródłowy | URL |
| DOI | 10.3390/electronics14152954 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Electronics |
Abstract
In recent years, the classification of underwater images has become one of the most remarkable areas of research in computer vision due to its useful applications in marine sciences, aquatic robotics, and sea exploration. Underwater imaging is pivotal for the evaluation of marine eco-systems, analysis of biological habitats, and monitoring underwater infrastructure. Extracting useful information from underwater images is highly challenging due to factors such as light distortion, scattering, poor contrast, and complex foreground patterns. These difficulties make traditional image processing and machine learning techniques struggle to analyze images accurately. As a result, these challenges and complexities make the classification difficult or poor to perform. Recently, deep learning techniques, especially convolutional neural network (CNN), have emerged as influential tools for underwater image classification, contributing noteworthy improvements in accuracy and performance in the presence of all these challenges. In this paper, we have proposed a modified ResNet-18 model for the binary classification of underwater images into raw and enhanced images. In the proposed modified ResNet-18 model, we have added new layers such as Linear, rectified linear unit (ReLU) and dropout layers, arranged in a block that was repeated three times to enhance feature extraction and improve learning. This enables our model to learn the complex patterns present in the image in more detail, which helps the model to perform the classification very well. Due to these newly added layers, our proposed model addresses various complexities such as noise, distortion, varying illumination conditions, and complex patterns by learning vigorous features from underwater image datasets. To handle the issue of class imbalance present in the dataset, we applied a data augmentation technique. The proposed model achieved outstanding performance, with 96% accuracy, 99% precision, 92% sensitivity, 99% specificity, 95% F1-score, and a 96% Area under the Receiver Operating Characteristic Curve (AUC-ROC) score. These results demonstrate the strength and reliability of our proposed model in handling the challenges posed by the underwater imagery and making it a favorable solution for advancing underwater image classification tasks.
