PPANet: Point-Wise Pyramid Attention Network for Semantic Segmentation

Mohammed A. M. Elhassan, YuXuan Chen, Yunyi Chen, Chenxi Huang, Jane Yang, Xingcong Yao, Chenhui Yang, Yinuo Cheng
2021 Wireless Communications and Mobile Computing  
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
more » ... ation 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.
doi:10.1155/2021/5563875 doaj:6555263df5c84f7f988d4053ae8f5c75 fatcat:hsq2tef2mrbllo4o2geksm6ufe