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


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. The experimental 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: College of Engineering & Physical Sciences > Adaptive communications networks research group
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic 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.
Uncontrolled Keywords: Machine learning,autonomous vehicles,intelligent vehicles,object recognition,Automotive Engineering,Mechanical Engineering,Computer Science Applications
Publication ISSN: 1558-0016
Full Text Link:
Related URLs: https://ieeexpl ... ocument/8694965 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-04-01
Published Online Date: 2019-04-22
Accepted Date: 2019-04-15
Authors: Wei, Jian
He, Jianhua (ORCID Profile 0000-0002-5738-8507)
Zhou, Yi
Chen, Kai
Tang, Zuoyin (ORCID Profile 0000-0001-7094-999X)
Xiong, Zhiliang



Version: Accepted Version

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