Night-Time Vehicle Detection Based on Hierarchical Contextual Information
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Journal / Publication | IEEE Transactions on Intelligent Transportation Systems |
Volume | 25 |
Issue number | 10 |
Online published | 17 May 2024 |
Publication status | Published - Oct 2024 |
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DOI | DOI |
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Attachment(s) | Documents
Publisher's Copyright Statement
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85193546695&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(ff4ed8d2-764f-48da-962f-0e3399883c45).html |
Abstract
Night-time vehicle detection, which forms a basic
component of the intelligent transportation system, is a topic
of intense research interest with multifarious challenges. Due
to the presence of low-light conditions, vehicles are typically
indistinguishable from the background, and interference from
light sources often arises in this complex environment. Existing
widely used deep learning-based object detection models are
designed for daytime scenarios and they have seldom considered
these problems. Based on an investigation of current detection
techniques and an analysis of the specific challenges of nighttime vehicle detection, we propose a hierarchical contextual
information (HCI) framework that can be used as a plugand-play component to improve existing deep learning-based
detection models under night-time conditions. Our HCI consists
of three parts, an estimation branch, a segmentation branch and
a detection branch, and can be applied to excavate hierarchical
contextual clues and fuse them for the detection of vehicles
in night-time environments. In each module, the context and
predictions are extracted at the image-level, the pixel-level, and
the object-level, respectively, and the results from each are
complementary and beneficial to each other. Comprehensive
experiments on two scenes from the Berkeley Deep Drive (BDD)
dataset are presented to demonstrate the flexibility and generalization ability of our HCI. The significant improvements offered
by HCI over main-stream detectors such as YOLOX, Faster
RCNN, SSD, and EfficientDet also highlight the effectiveness of
our approach for night-time vehicle detection.
© 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
© 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Research Area(s)
- Deep neural network, context learning, intelligent transportation systems, low-light traffic image, night-time vehicle detection
Citation Format(s)
Night-Time Vehicle Detection Based on Hierarchical Contextual Information. / Zhang, Houwang; Yang, Kai-Fu; Li, Yong-Jie et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 25, No. 10, 10.2024.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 25, No. 10, 10.2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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