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Journals / Balkan Journal of Electrical and Computer Engineering / 2026 / Cilt: 13 Sayı: 1

Classification of Images in Bad Weather Conditions with Convolutional Neural Networks

Yasin Demir(ERZURUM TECHNICAL UNIVERSITY)0000-0002-0834-2780
Nagihan Severoğlu(ERZURUM TECHNICAL UNIVERSITY)0000-0002-3524-2566
Nur Hüseyin Kaplan(ERZURUM TECHNICAL UNIVERSITY)0000-0002-4740-3259
Sefa Küçük(ERZURUM TECHNICAL UNIVERSITY)0000-0002-0279-3185
Pages
39–46
Publication Date
2025-03-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed network outperforms the existing methods with 95.50% accuracy for a classification problem with five different scenarios.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Weather conditions are one of the major factors significantly influencing the daily lives of individuals.
📊
Key Findings & Data:Unfavorable weather conditions adversely affect their lives and directly impede the progress of the subsequent image-processing steps necessary for real-world vision tasks such as object detection and autonomous driving.
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Conclusion & Impact:The proposed network outperforms the existing methods with 95.50% accuracy for a classification problem with five different scenarios.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Multi-class classification · deep learning · convolutional neural networks

Abstract

Weather conditions are one of the major factors significantly influencing the daily lives of individuals. Unfavorable weather conditions adversely affect their lives and directly impede the progress of the subsequent image-processing steps necessary for real-world vision tasks such as object detection and autonomous driving. For this reason, the correct classification of the weather conditions is of great importance. Although traditional classification methods achieve high accuracy in various tasks, they cannot achieve the same success in classifying weather conditions. In this paper, we propose a novel convolutional neural network (CNN) framework for the classification of weather conditions with high accuracy. The proposed network outperforms the existing methods with 95.50% accuracy for a classification problem with five different scenarios.

🔖

Cite This Article

Demir, Y., Severoğlu, N., Kaplan, N. H., Küçük, S. (2025). Classification of Images in Bad Weather Conditions with Convolutional Neural Networks. Balkan Journal of Electrical and Computer Engineering, Cilt: 13 Sayı: 1, 39–46. https://doi.org/10.17694/bajece.1415025

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