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Sensors 2018, 18(6), 1913;

Effective Vehicle-Based Kangaroo Detection for Collision Warning Systems Using Region-Based Convolutional Networks

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Victoria 3216, Australia
Author to whom correspondence should be addressed.
Received: 25 April 2018 / Revised: 24 May 2018 / Accepted: 8 June 2018 / Published: 12 June 2018
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: kangaroo collision; kangaroo detection; collision avoidance; kangaroo dataset kangaroo collision; kangaroo detection; collision avoidance; kangaroo dataset

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

  • Externally hosted supplementary file 1
    Description: The dataset that we used for training and testing our models are now available through the following link:

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Saleh, K.; Hossny, M.; Nahavandi, S. Effective Vehicle-Based Kangaroo Detection for Collision Warning Systems Using Region-Based Convolutional Networks. Sensors 2018, 18, 1913.

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