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1- Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Semnan
2- Department of Electrical Engineering - Control, Faculty of Engineering, Imam Khomeini International University, Qazvin
2- Department of Electrical Engineering - Control, Faculty of Engineering, Imam Khomeini International University, Qazvin
Abstract: (411 Views)
Underwater images play a vital role in marine environmental research. Accurate analysis of these images is essential for managing marine ecosystems, preserving biodiversity, and monitoring environmental changes. However, various factors such as poor lighting, quality degradation due to light absorption and scattering, noise from suspended particles, and color distortions pose significant challenges in processing underwater images. These issues reduce the effectiveness of traditional image processing methods. Deep learning, as a powerful approach for automatically extracting complex features, can help mitigate or overcome these challenges. In this study, an intelligent deep learning method based on Capsule Neural Networks (CapsNets) is proposed for underwater image classification. By preserving spatial relationships among features and reducing reliance on pooling operations, CapsNets offer a better understanding of complex patterns. These characteristics enable effective handling of underwater image processing challenges. The proposed model, utilizing an advanced architecture, achieved higher classification accuracy compared to conventional methods and demonstrated robust performance under varying lighting conditions and image qualities. Experimental results showed that the proposed model outperformed existing approaches, achieving an overall accuracy of 96.75%. Moreover, the model exhibited stable performance across different underwater image classes, with accuracy ranging from 95.5% (lowest) to 98% (highest). Additionally, the average accuracy, sensitivity, and F1-score of the model were calculated as 96.75%, 96.75%, and 96.73%, respectively, indicating the model’s robustness in underwater image classification. Based on these findings, the proposed model demonstrates strong potential for a wide range of applications, including marine habitat monitoring, underwater exploration, conservation of rare species, and tracking environmental changes.
Keywords: Underwater Images, Capsule Neural Network, Image Classification, Underwater Image Processing, Deep Learning
Type of Study: Research Paper |
Subject:
Environmental Study
Received: 2025/03/8 | Accepted: 2025/10/6
Received: 2025/03/8 | Accepted: 2025/10/6
فایل صوتی چکیده مقاله [MP3 3476 KB] (10 Download)
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