From ImageNet to mining: Adapting visual object detection with minimal supervision
Bewley, Alex & Upcroft, Ben (2015) From ImageNet to mining: Adapting visual object detection with minimal supervision. In Proceedings of the 10th International Conference on Field and Service Robotics (FSR), Springer-Verlag, University of Toronto, Canada. (In Press)
This paper presents visual detection and classification of light vehicles and personnel on a mine site.We capitalise on the rapid advances of ConvNet based object recognition but highlight that a naive black box approach results in a significant number of false positives. In particular, the lack of domain specific training data and the unique landscape in a mine site causes a high rate of errors. We exploit the abundance of background-only images to train a k-means classifier to complement the ConvNet. Furthermore, localisation of objects of interest and a reduction in computation is enabled through region proposals. Our system is tested on over 10km of real mine site data and we were able to detect both light vehicles and personnel. We show that the introduction of our background model can reduce the false positive rate by an order of magnitude.
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|Item Type:||Conference Paper|
|Keywords:||Robotic vision, Mining industry, Autonomous vehicles, Visual detection, Light vehicles, ConvNet|
|Divisions:||Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 Springer|
|Deposited On:||15 May 2015 00:39|
|Last Modified:||28 Jan 2017 21:25|
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