Multi-Receptive Atrous Convolutional Network for Semantic Segmentation

Deep Convolutional Neural Networks (DCNNs) have enhanced the performance of semantic image segmentation but many challenges still remain. Specifically, some details may be lost due to the downsampling operations in DCNNs. Furthermore, objects may appear in an image at different scales, and extracting features using convolutional filters with large sizes is costly in computation. Moreover, in many cases, contextual information, such as global and background features, is potentially useful for semantic segmentation. In this paper, we address these challenges by proposing a Multi-Receptive Atrous Convolutional Network (MRACN) for semantic image segmentation. The proposed MRACN captures the multi-receptive features and the global features at different receptive scales of the input. MRACN can serve as a module easily being integrated into existing models. We adapt the ResNet-101 model as the backbone network and further propose a MRACN segmentation model (MRACN-Seg). The experimental results demonstrate the effectiveness of the proposed model on two datasets: a benchmark dataset (PASCAL VOC 2012) and our industry dataset.