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Extended Density-aware Cross-scale Transformer for Multimodal Atmospheric Degradation in Robust Object Classification
Fiston Oshasha Oshasha, Francklin Mwamba Kande, Saint Jean Djungu, Muka Kabeya Arsene, Jacques IloloIpan, Ruben Mfunyi Kabongo
Pages - 210 - 233 | Revised - 15-12-2025 | Published - 31-12-2025
MORE INFORMATION
KEYWORDS
Computer Vision, Atmospheric Degradation, Transformer Architecture, Multi-modal
Learning, Robust Classification, Weather Conditions, Density-aware Networks.
ABSTRACT
Real world computer vision systems face significant performance degradation under adverse
conditions. Building on our previous EDCST framework for fog-degraded imagery, this work
introduces EDCST-MM (Multi-Modal), an extended architecture capable of handling 16
atmospheric and visual degradation conditions simultaneously. Unlike traditional vision systems
that require condition-specific models, EDCST-MM leverages unified density-aware encoding,
cross-scale feature fusion, and adaptive transformer blocks to achieve robust classification
across fog, rain, darkness, blur, and noise scenarios.
This work addresses the fundamental research question: Can a unified deep learning architecture handle diverse atmospheric and visual degradations without requiring condition-specific models or pre-processing restoration pipelines, while maintaining both robustness and computational efficiency for real-world deployment?
Evaluated on the CODaN dataset, the model reaches an average accuracy of 92.78%, representing an 18.6% improvement over the best baseline (DeiT-S: 74.2%). The framework demonstrates exceptional robustness on atmospheric degradations (fog: 98.24%, rain: 97.73%, darkness: 97.64%) and strong performance under visual degradations (blur: 95.22%, structured noise: 85.90%). Accuracy remains above 95% on 13 of 16 conditions, though Gaussian noise remains challenging (47.80%).
These results validate the effectiveness of our multi-condition density encoding and conditionaware attention mechanisms while maintaining computational efficiency (21.3M parameters, 12ms GPU inference). EDCST-MM thus establishes a clear advance over existing approaches and represents a practical step toward deploying robust vision systems in real-world multidegraded environments.
This work addresses the fundamental research question: Can a unified deep learning architecture handle diverse atmospheric and visual degradations without requiring condition-specific models or pre-processing restoration pipelines, while maintaining both robustness and computational efficiency for real-world deployment?
Evaluated on the CODaN dataset, the model reaches an average accuracy of 92.78%, representing an 18.6% improvement over the best baseline (DeiT-S: 74.2%). The framework demonstrates exceptional robustness on atmospheric degradations (fog: 98.24%, rain: 97.73%, darkness: 97.64%) and strong performance under visual degradations (blur: 95.22%, structured noise: 85.90%). Accuracy remains above 95% on 13 of 16 conditions, though Gaussian noise remains challenging (47.80%).
These results validate the effectiveness of our multi-condition density encoding and conditionaware attention mechanisms while maintaining computational efficiency (21.3M parameters, 12ms GPU inference). EDCST-MM thus establishes a clear advance over existing approaches and represents a practical step toward deploying robust vision systems in real-world multidegraded environments.
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Dr. Fiston Oshasha Oshasha
General Commissariat for Atomic Energy, Regional Center for Nuclear Studies of Kinshasa, Kinshasa - Democratic Republic of the Con
fiston.oshasha.oshasha@cgea-rdc.org
Mr. Francklin Mwamba Kande
Health Sciences Research Institute, Kinshasa - Democratic Republic of Congo
Mr. Saint Jean Djungu
Center for Research in Applied Computing, Kinshasa - Democratic Republic of the Con
Mr. Muka Kabeya Arsene
General Commissariat for Atomic Energy, Regional Center for Nuclear Studies of Kinshasa, Kinshasa - Democratic Republic of the Con
Mr. Jacques IloloIpan
Faculty of Science and Technology, University of Kinshasa, Kinshasa - Democratic Republic of the Con
Mr. Ruben Mfunyi Kabongo
Faculty of Science and Technology, University of Kinshasa, Kinshasa - Democratic Republic of the Con
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