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Research on Scene Parsing Algorithm Cascading Object Detection Network

机译:场景解析算法级联对象检测网络的研究

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Using deep learning for visual scene parsing will satisfy the demand of the next generation of automatic driving technology. However, current parsing algorithms are not mature enough for practical applications unless higher accuracy and efficiency are obtained. We propose a novel scene parsing algorithm framework which integrates the object detection technologies into convolution neural network to improve the overall effectiveness. The framework consists of three components: i) a scene parsing network presenting primary semantic segmentation result. ii)an object detection network calculating the location and confidence of the targets in images. iii) an integration and filter module that cascades previous two results. Extensive experiments suggest that our model is capable of practical use and achieving more favorable scene parsing performance of mIoU score as 69.4% on CamVid dataset.
机译:利用深度学习的视觉场景解析将满足下一代自动驾驶技术的需求。然而,除非获得更高的精度和效率,否则当前解析算法不够成熟。除非获得更高的精度和效率。我们提出了一种新颖的场景解析算法框架,将物体检测技术集成到卷积神经网络中,以提高整体效率。该框架由三个组件组成:i)呈现主要语义分段结果的场景解析网络。 ii)对象检测网络计算图像中目标的位置和置信度。 III)集成和过滤器模块,其级联前两种结果。广泛的实验表明,我们的模型能够实际使用,并在Camvid数据集中实现Miou评分的更有利的场景解析性能为69.4 \%。

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