Enhanced Object Detection with Deep Convolutional Neural Networks for Advanced Driving Assistance

Wei, Jian, He, Jianhua, Zhou, Yi, Chen, Kai, Tang, Zuoyin and Xiong, Zhiliang (2019). Enhanced Object Detection with Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems ,

Abstract

Object detection is a critical problem for advanced driving assistance systems (ADAS). Recently convolutional neural networks (CNN) achieved large successes on object detection, with performance improvement over traditional approaches, which use hand-engineered features. However, due to the challenging driving environment (e.g., large object scale variation, object occlusion and bad light conditions), popular CNN detectors do not achieve very good object detection accuracy over the KITTI autonomous driving benchmark dataset. In this paper we propose three enhancements for CNN based visual object detection for ADAS. To address the large object scale variation challenge, deconvolution and fusion of CNN feature maps are proposed to add context and deeper features for better object detection at low feature map scales. In addition, soft non-maximal suppression (NMS) is applied across object proposals at different feature scales to address the object occlusion challenge. As the cars and pedestrians have distinct aspect ratio features, we measure their aspect ratio statistics and exploit them to set anchor boxes properly for better object matching and localization. The proposed CNN enhancements are evaluated with various image input sizes by experiments over KITTI dataset. Experiment results demonstrate the effectiveness of the proposed enhancements with good detection performance over KITTI test set.

Publication DOI: https://doi.org/10.1109/TITS.2019.2910643
Divisions: Engineering & Applied Sciences > Adaptive communications networks research group
Engineering & Applied Sciences > Electrical, electronic & power engineering
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Related URLs: https://ieeexpl ... ocument/8694965 (Publisher URL)
Accepted Date: 2019-04-15
Published Online Date: 2019-04-22
Authors: Wei, Jian
He, Jianhua ( 0000-0002-5738-8507)
Zhou, Yi
Chen, Kai
Tang, Zuoyin ( 0000-0001-7094-999X)
Xiong, Zhiliang

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