Optimized HRNet for image semantic segmentation

With the rapid development of deep learning, image semantic segmentation has made great progress and become a hot topic in scene understanding of computer vision. In this paper, we propose an optimized high-resolution net (HRNet) for image semantic segmentation. Unlike traditional networks usually extract feature maps based on a high-to-low encoder, which may easily loss important shape and boundary details especially for the deeper layers with lower resolutions, our optimized HRNet can maintain high resolution features at all times using a relatively shallow and parallel network structure. To improve the ability of our model in better recognizing the objects with various scales and irregular shapes, we introduce a mixed dilated convolution (MDC) module, which can not only increase the diversity of the receptive fields, but also tackle the “gridding” problem commonly existing in the conventional dilated convolution. By minimizing fine detail lost based on a DUpsample strategy, we further develop a multi-level data-dependent feature aggregation (MDFA) module to enhance the capability of our network in better identifying the fine details especially for the small objects with fuzzy boundaries. We evaluate the optimized HRNet on four different datasets, including Cityscapes, Pascal VOC2012, CamVid and the KITTI. Experimental results validate the effectiveness of our method in improving the accuracy of image semantic segmentation. Comparisons with state-of-the-art methods also verify the advantages of our optimized HRNet in achieving better semantic segmentation performance.