In recent years, convolutional neural networks (CNNs) have been at the centre of the advances and progress of advanced driver assistance systems and autonomous driving. This paper presents a point-wise pyramid attention network, namely, PPANet, which employs an encoder-decoder approach for semantic segmentation. Specifically, the encoder adopts a novel squeeze nonbottleneck module as a base module to extract feature representations, where squeeze and expansion are utilized to obtain high segmentation accuracy. An upsampling module is designed to work as a decoder; its purpose is to recover the lost pixel-wise representations from the encoding part. The middle part consists of two parts point-wise pyramid attention (PPA) module and an attention-like module connected in parallel. The PPA module is proposed to utilize contextual information effectively. Furthermore, we developed a combined loss function from dice loss and binary cross-entropy to improve accuracy and get faster training convergence in KITTI road segmentation. The paper conducted the training and testing experiments on KITTI road segmentation and Camvid datasets, and the evaluation results show that the proposed method proved its effectiveness in road semantic segmentation.

中文翻译：

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PPANet：用于语义分割的点智慧金字塔注意网络

近年来，卷积神经网络（CNN）一直是高级驾驶员辅助系统和自动驾驶技术进步与发展的中心。本文提出了一种逐点金字塔注意力网络，即PPANet，它使用编码器-解码器方法进行语义分割。具体地，该编码器采用新颖的挤压非瓶颈模块作为基本模块来提取特征表示，其中利用挤压和扩展来获得较高的分割精度。上采样模块旨在用作解码器；其目的是从编码部分恢复丢失的逐像素表示。中间部分由两部分组成：点式金字塔注意力（PPA）模块和并行连接的类似注意力的模块。提出PPA模块以有效利用上下文信息。此外，我们从骰子损失和二进制交叉熵开发了组合损失函数，以提高准确性并在KITTI道路分割中获得更快的训练收敛性。本文对KITTI道路分割和Camvid数据集进行了训练和测试实验，评估结果表明，该方法证明了其在道路语义分割中的有效性。