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

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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.

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  • Enhanced Object Detection with Deep Convolutional Neural Networks

    Rights statement: © 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.

    Accepted author manuscript, 7 MB, PDF-document

Details

Original languageEnglish
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Early online date22 Apr 2019
DOIs
Publication statusE-pub ahead of print - 22 Apr 2019

Bibliographic note

© 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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