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.

本研究通过模型发展和领域适应的方法研究了使用真实世界和合成数据来处理语义分割问题的有效性，通过应用高分辨率网络（HRNet）、对象上下文表示（OCR）和分层多尺度注意力（HMA）等技术，以及基于领域的批量归一化（DNB）降低分布偏移，提出了一种在各种不同室外情况下表现良好的鲁棒模型， 在验证集上达到了81.259的平均交并比（mIoU）。

真实世界和合成车辆前向摄像头图像的语义分割