Using the wide-range attention U-Net for road segmentation

ABSTRACT

Recently, the U-Net-like convolutional neural network architecture composed of encoder, decoder, and symmetric skip-connection has shown impressive performance and has quickly become the de-facto standard in road segmentation tasks. In this letter, a novel Wide-Range Attention Unit (WRAU) is introduced and incorporated into a densely-connected U-Net architecture which we term the Wide-Range Attention U-Net (WRAU-Net). New architecture achieves a wide-range channel-wise attention mechanism by two major contributions. Firstly, we add a partially dense-connections branch to the U-Net architecture to aggregate informative feature map channels with multiple spatial resolutions from different levels of encoder. Secondly, before encoding back into the network, the WRAU is employed at the cluster node of partially dense-connections to learn to weight aggregated feature map channels by a three-layer convolutional network. We evaluate our WRAU-Net on the Massachusetts road segmentation benchmark. The cross-validation results show that the employment of the proposed attention mechanism to classic U-Net architecture gives a tangible boost to test accuracy when compared to U-Net itself and another two state-of-the-art baselines.