用深度学习分割驾驶舱图像，提升混合现实沉浸感
Applying Deep Learning for cockpit segmentation in the context of mixed reality
- 采用U-net和DeepLabV3+进行驾驶舱图像语义分割
- 在CAT793F模拟器数据上达到约90%的分割准确率
- 适用于虚拟与真实场景融合的工业级混合现实应用
计算机视觉持续发展，第一人称视角技术推动新机遇。混合现实通过实时呈现物理世界对象实现虚实融合，关键在于提升用户沉浸感，使其更贴近理想现实。本文针对此目标，利用离线越野卡车模拟器CAT793F的摄像头采集真实图像，结合人工智能技术实现图像分割，以区分前景与背景，促进虚实图像融合。实验采用卷积神经网络架构U-net和DeepLabV3+进行图像分割，结果表明模型整体准确率接近90%，并确定了最优模型。
原文摘要 · Abstract (English)
Computer vision is an area that has been growing continuously. With the advance of technologies with a first-person view, new development opportunities have emerged inside the area. Mixed reality promotes virtual environments with objects from the physical world shown in real time. For that, it's necessary to be concerned with the immersion of the user in this simulated environment, increasingly seeking to bring it closer to a possible desired reality. This paper proposes the development of image processing in order to perform the segmentation of images to identify what is foreground and background in order to facilitate the union of virtual and real images. Thus, the present work obtain real images of the user using the off-highway truck simulator CAT793F, through a camera, to be able to perform the segmentation of such images with artificial intelligence techniques.The convolutional neural network architectures "U-net" and "DeepLabV3+" are applied to perform image segmentation. As a result, metrics with around 90% accuracy were presented and and the best model was determined.
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