Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation

Alshammari, N.; Akcay, S.; Breckon, T.P.

Abstract

Robust semantic scene segmentation for automotive applications is a challenging problem in two key aspects: (1) labelling every individual scene pixel and (2) performing this task under unstable weather and illumination changes (e.g., foggy weather), which results in poor outdoor scene visibility. Such visibility limitations lead to non-optimal performance of generalised deep convolutional neural network-based semantic scene segmentation. In this paper, we propose an efficient endto-end automotive semantic scene understanding approach that is robust to foggy weather conditions. As an end-to-end pipeline, our proposed approach provides: (1) the transformation of imagery from foggy to clear weather conditions using a domain transfer approach (correcting for poor visibility) and (2) semantically segmenting the scene using a competitive encoderdecoder architecture with low computational complexity (enabling real-time performance). Our approach incorporates RGB colour, depth and luminance images via distinct encoders with dense connectivity and features fusion to effectively exploit information from different inputs, which contributes to an optimal feature representation within the overall model. Using this architectural formulation with dense skip connections, our model achieves comparable performance to contemporary approaches at a fraction of the overall model complexity.

Citation

Alshammari, N., Akcay, S., & Breckon, T. (2021, July). Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation. Presented at IEEE Intelligent Transportation Systems Society

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE Intelligent Transportation Systems Society
• Acceptance Date: Apr 23, 2021
• Online Publication Date: Jul 11, 2021
• Publication Date: 2021-07
• Deposit Date: May 23, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Public URL: https://durham-repository.worktribe.com/output/1140852
• Publisher URL: https://breckon.org/toby/publications/papers/alshammari21multimodal.pdf