Skip to main content
padlock icon - secure page this page is secure

Open Access Real-time Small-object Change Detection from Ground Vehicles Using a Siamese Convolutional Neural Network

Download Article:
 Download
(HTML 141.8 kb)
 
or
 Download
(PDF 2,164.7 kb)
 
Abstract

Detecting changes in an uncontrolled environment using cameras mounted on a ground vehicle is critical for the detection of roadside Improvised Explosive Devices (IEDs). Hidden IEDs are often accompanied by visible markers, whose appearances are a priori unknown. Little work has been published on detecting unknown objects using deep learning. This article shows the feasibility of applying convolutional neural networks (CNNs) to predict the location of markers in real time, compared to an earlier reference recording. The authors investigate novel encoder‐decoder Siamese CNN architectures and introduce a modified double-margin contrastive loss function, to achieve pixel-level change detection results. Their dataset consists of seven pairs of challenging real-world recordings, and they investigate augmentation with artificial object data. The proposed network architecture can compare two images of 1920 × 1440 pixels in 27 ms on an RTX Titan GPU and significantly outperforms state-of-the-art networks and algorithms on our dataset in terms of F-1 score by 0.28.

13 References.

No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: Eindhoven University of Technology, SPS-VCA Group of Electr. Eng., Groene Loper 3, 5612 AE Eindhoven, the Netherlands

Publication date: November 1, 2019

This article was made available online on December 16, 2019 as a Fast Track article with title: "Real-time Small-object Change Detection from Ground Vehicles Using a Siamese Convolutional Neural Network".

More about this publication?
  • The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.

    IS&T's JIST-first publication option allows authors wishing to present their work at conferences, but have a journal citation for their paper, to submit a paper to JIST that follows the same rigorous peer-review vetting and publication process as traditional JIST articles, but with the benefit of a condensed time-to-publication time frame and guaranteed conference presentation slot.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

  • Editorial Board
  • Information for Authors
  • Submit a Paper
  • Subscribe to this Title
  • Membership Information
  • Information for Advertisers
  • Terms & Conditions
  • Privacy Policy
  • Information for JIST-First Authors
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more