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Proceedings Paper

Real-time object detection and semantic segmentation for autonomous driving
Author(s): Baojun Li; Shun Liu; Weichao Xu; Wei Qiu
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Paper Abstract

In this paper, we proposed a Highly Coupled Network (HCNet) for joint objection detection and semantic segmentation. It follows that our method is faster and performs better than the previous approaches whose decoder networks of different tasks are independent. Besides, we present multi-scale loss architecture to learn better representation for different scale objects, but without extra time in the inference phase. Experiment results show that our method achieves state-of-the-art results on the KITTI datasets. Moreover, it can run at 35 FPS on a GPU and thus is a practical solution to object detection and semantic segmentation for autonomous driving.

Paper Details

Date Published: 19 February 2018
PDF: 8 pages
Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080P (19 February 2018);
Show Author Affiliations
Baojun Li, Guangdong Univ. of Technology (China)
Shun Liu, Guangdong Univ. of Technology (China)
Weichao Xu, Guangdong Univ. of Technology (China)
Wei Qiu, State Univ. of New York (United States)

Published in SPIE Proceedings Vol. 10608:
MIPPR 2017: Automatic Target Recognition and Navigation
Jianguo Liu; Jayaram K. Udupa; Hanyu Hong, Editor(s)

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