Citation

Manivannan, Parthasarathi and Sathyaprakash, Palaniyappan and Jayakumar, Vaithiyashankar and Chandrasekaran, Jayakumar and Ananthanarayanan, Bragadeesh Srinivasan and Sayeed, Md. Shohel (2024) Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation. Computers, Materials & Continua, 81 (3). pp. 4327-4347. ISSN 1546-2226

Text TSP_CMC_55628.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness. However, accurately classifying diverse and complex weather conditions remains a significant challenge. While advanced techniques such as Vision Transformers have been developed, they face key limitations, including high computational costs and limited generalization across varying weather conditions. These challenges present a critical research gap, particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’ intricate and dynamic nature in real-time. To address this gap, we propose a Multi-level Knowledge Distillation (MLKD) framework, which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead. Specifically, we employ ResNet50V2 and EfficientNetV2B3 as teacher models, known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network (CNN) student model. This framework balances the trade-off between high classification accuracy and efficient resource consumption, ensuring real-time applicability in autonomous systems. Our Response-based Multi-level Knowledge Distillation (R-MLKD) approach effectively transfers rich, high-level feature representations from the teacher models to the student model, allowing the student to perform robustly with significantly fewer parameters and lower computational demands. The proposed method was evaluated on three public datasets (DAWN, BDD100K, and CITS traffic alerts), each containing seven weather classes with 2000 samples per class. The results demonstrate the effectiveness of MLKD, achieving a 97.3% accuracy, which surpasses conventional deep learning models. This work improves classification accuracy and tackles the practical challenges of model complexity, resource consumption, and real-time deployment, offering a scalable solution for weather classification in autonomous driving systems.

Item Type:	Article
Uncontrolled Keywords:	EfficientNetV2B3; multi-level knowledge distillation; RestNet50V2; weather classification
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jan 2025 05:43
Last Modified:	03 Jan 2025 05:43
URII:	http://shdl.mmu.edu.my/id/eprint/13284

Downloads

Edit (login required)