papers AI Learner
The Github is limit! Click to go to the new site.

An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions

2019-02-20
Sercan Türkmen, Janne Heikkilä

Abstract

Assigning a label to each pixel in an image, namely semantic segmentation, has been an important task in computer vision, and has applications in autonomous driving, robotic navigation, localization, and scene understanding. Fully convolutional neural networks have proved to be a successful solution for the task over the years but most of the work being done focuses primarily on accuracy. In this paper, we present a computationally efficient approach to semantic segmentation, meanwhile achieving a high mIOU, $70.33\%$ on Cityscapes challenge. The network proposed is capable of running real-time on mobile devices. In addition, we make our code and model weights publicly available.

Abstract (translated by Google)
URL

http://arxiv.org/abs/1902.07476

PDF

http://arxiv.org/pdf/1902.07476


Similar Posts

Comments