WideSegNeXt: Semantic Image Segmentation Using Wide Residual Network and NeXt Dilated Unit

Semantic segmentation is widely applied in autonomous driving, in robotic picking, and for medical purposes. Due to the breakthrough of deep learning in recent years, the fully convolutional network (FCN)-based method has become the de facto standard in semantic segmentation. However, the simple FCN has difficulty in capturing global context information, since the local receptive field is small. Furthermore, there is a problem of low image resolution because of the existence of the pooling layer. In this paper, we address the shortcomings of the FCN by proposing a new architecture called WideSegNeXt, which captures the image context on various spatial scales and is effective in identifying small objects. In addition, there is little loss of position information, since there are no pooling layers in the structure. The proposed method achieves a mean intersection over union (MIoU) of 72.5% and a global accuracy (GA) of 92.4% on the CamVid dataset and achieves higher performance than previous methods without additional input datasets.

Automated summary

This paper proposed a new architecture called WideSegNeXt to address the shortcomings of the FCN in semantic segmentation, which can capture image context on various spatial scales, effectively identify small objects, and reduce the loss of position information. The proposed method achieved a mean intersection over union (MIoU) of 72.5% and a global accuracy (GA) of 92.4% on the CamVid dataset without the need for additional input datasets, outperforming previous methods.