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Multiple Skip Connections of Dilated Convolution Network for Semantic Segmentation

Semantic segmentation is a task to estimate class for each pixel. This task also have received benefit from the deep ConvNet and it has achieves high accuracy. In the semantic segmentation from in-vehicle camera image, the object size such as a pedestrian or a vehicle fluctuates according to the distance from the camera. We propose scale aware semantic segmentation method especially small object. The contributions of the method are 1) to feed the features of small region by multiple skip connections, 2) to extract context from multiple receptive field by multiple dilated convolution blocks. The proposed method has achieved high accuracy in the Cityscapes dataset. The comparison with state-of-the-art methods, it has achieved the comparable performance at category IoU and iIoU metrics.