19 February 2018 Real-time object detection and semantic segmentation for autonomous driving
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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.
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Baojun Li, Shun Liu, Weichao Xu, 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); doi: 10.1117/12.2288713; https://doi.org/10.1117/12.2288713
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