Openly accessible

Effective vehicle-based kangaroo detection for collision warning systems using region-based convolutional networks

Saleh, Khaled, Hossny, Mohammed and Nahavandi, Saeid 2018, Effective vehicle-based kangaroo detection for collision warning systems using region-based convolutional networks, Sensors, vol. 18, no. 6, pp. 1-15, doi: 10.3390/s18061913.

Attached Files
Name Description MIMEType Size Downloads
nahavandi-effectivevehicle-2018.pdf Published version application/pdf 2.35MB 9

Title Effective vehicle-based kangaroo detection for collision warning systems using region-based convolutional networks
Author(s) Saleh, Khaled
Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-1593-6296
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Sensors
Volume number 18
Issue number 6
Article ID 1913
Start page 1
End page 15
Total pages 15
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2018-06-12
ISSN 1424-8220
Keyword(s) kangaroo collision
kangaroo detection
collision avoidance
kangaroo dataset
Accidents, Traffic
Animals
Macropodidae
Neural Networks (Computer)
Support Vector Machine
Science & Technology
Physical Sciences
Technology
Chemistry, Analytical
Electrochemistry
Instruments & Instrumentation
Chemistry
Summary Traffic collisions between kangaroos and motorists are on the rise on Australian roads. According to a recent report, it was estimated that there were more than 20,000 kangaroo vehicle collisions that occurred only during the year 2015 in Australia. In this work, we are proposing a vehicle-based framework for kangaroo detection in urban and highway traffic environment that could be used for collision warning systems. Our proposed framework is based on region-based convolutional neural networks (RCNN). Given the scarcity of labeled data of kangaroos in traffic environments, we utilized our state-of-the-art data generation pipeline to generate 17,000 synthetic depth images of traffic scenes with kangaroo instances annotated in them. We trained our proposed RCNN-based framework on a subset of the generated synthetic depth images dataset. The proposed framework achieved a higher average precision (AP) score of 92% over all the testing synthetic depth image datasets. We compared our proposed framework against other baseline approaches and we outperformed it with more than 37% in AP score over all the testing datasets. Additionally, we evaluated the generalization performance of the proposed framework on real live data and we achieved a resilient detection accuracy without any further fine-tuning of our proposed RCNN-based framework.
Language eng
DOI 10.3390/s18061913
Indigenous content off
Field of Research 080106 Image Processing
0906 Electrical and Electronic Engineering
Socio Economic Objective 880109 Road Safety
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30110956

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 62 Abstract Views, 11 File Downloads  -  Detailed Statistics
Created: Mon, 04 Mar 2019, 13:50:35 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.