ICNet for Real-Time Semantic Segmentation on High-Resolution Images

[guanfuchen]

related paper

摘要

We focus on the challenging task of realtime semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an compressed-PSPNet-based image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion to quickly achieve high-quality segmentation. Our system yields realtime inference on a single GPU card with decent quality results evaluated on challenging Cityscapes dataset.

[guanfuchen]

Inference speed and mIoU performance on Cityscapes [5] test set. Methods involved are ResNet38 [30], PSPNet [33], DUC [29], RefineNet [14], LRR [6], FRRN [22], DeepLabv2 [3], Dilation10 [32], DPN [18], FCN-8s [19], DeepLab [2], CRF-RNN [34], SQ [28], ENet [21], SegNet [1], and our ICNet.