Real-time object detection and semantic segmentation for autonomous driving

Baojun Li; Shun Liu; Weichao Xu; Wei Qiu

doi:10.1117/12.2288713

19 February 2018 Real-time object detection and semantic segmentation for autonomous driving

Baojun Li, Shun Liu, Weichao Xu, Wei Qiu

Proceedings Volume 10608, MIPPR 2017: Automatic Target Recognition and Navigation; 106080P (2018) https://doi.org/10.1117/12.2288713
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

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.

Citation Download Citation

Baojun Li, Shun Liu, Weichao Xu, and Wei Qiu "Real-time object detection and semantic segmentation for autonomous driving", Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080P (19 February 2018); https://doi.org/10.1117/12.2288713

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