Semantic Segmentation for Real-World and Synthetic Vehicle's Forward-Facing Camera Images
<|reference_start|>Semantic Segmentation for Real-World and Synthetic Vehicle's Forward-Facing Camera Images: In this paper, we present the submission to the 5th Annual Smoky Mountains Computational Sciences Data Challenge, Challenge 3. This is the solution for semantic segmentation problem in both real-world and synthetic images from a vehicle s forward-facing camera. We concentrate in building a robust model which performs well across various domains of different outdoor situations such as sunny, snowy, rainy, etc. In particular, our method is developed with two main directions: model development and domain adaptation. In model development, we use the High Resolution Network (HRNet) as the baseline. Then, this baseline s result is processed by two coarse-to-fine models: Object-Contextual Representations (OCR) and Hierarchical Multi-scale Attention (HMA) to get the better robust feature. For domain adaption, we implement the Domain-Based Batch Normalization (DNB) to reduce the distribution shift from diverse domains. Our proposed method yield 81.259 mean intersection-over-union (mIoU) in validation set. This paper studies the effectiveness of employing real-world and synthetic data to handle the domain adaptation in semantic segmentation problem.<|reference_end|>
@article{nguyen2024semantic,
    title={Semantic Segmentation for Real-World and Synthetic Vehicle's
  Forward-Facing Camera Images},
    author={Tuan T. Nguyen, Phan Le, Yasir Hassan, Mina Sartipi},
    journal={arXiv preprint arXiv:2407.05452},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05452},
    primaryClass={cs.CV}
}