This paper proposes a deep learning-based efficient and compact solution for road scene segmentation problem, named deep residual coalesced convolutional network (RCC-Net). Initially, the RCC-Net performs dimensionality reduction to compress and extract relevant features, from which it is subsequently delivered to the encoder. The encoder adopts the residual network style for efficient model size. In the core of each residual network, three different convolutional layers are simultaneously coalesced for obtaining broader information. The decoder is then altered to upsample the encoder for pixel-wise mapping from the input images to the segmented output. Experimental results reveal the efficacy of the proposed network over the state-of-the-art methods and its capability to be deployed in an average system.

中文翻译：

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深度残差融合卷积网络用于有效的语义道路分割

本文提出了一种基于深度学习的高效紧凑的道路场景分割问题解决方案，称为深度残差联合卷积网络（RCC-Net）。最初，RCC-Net执行降维以压缩和提取相关特征，然后将其从中传递到编码器。编码器采用残差网络样式以实现有效的模型尺寸。在每个残差网络的核心中，同时合并三个不同的卷积层以获得更广泛的信息。然后更改解码器以对编码器进行上采样，以进行从输入图像到分段输出的逐像素映射。实验结果表明，所提出的网络在最新技术方法上的功效及其在普通系统中的部署能力。