Measuring and Evaluating Bitumen Coverage of Stones using two Different Digital Image Analysis Methods

Källén, Hanna; Heyden, Anders; Åström, Karl; Lindh, Per (2016). Measuring and Evaluating Bitumen Coverage of Stones using two Different Digital Image Analysis Methods. Measurement, 84, (April 2016), 56 - 67
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DOI:
| Published | English
Authors:
Källén, Hanna ; Heyden, Anders ; Åström, Karl ; Lindh, Per
Department:
Mathematics (Faculty of Engineering)
Engineering Mathematics (M.Sc.Eng.)
Centre for Mathematical Sciences
ELLIIT: the Linköping-Lund initiative on IT and mobile communication
eSSENCE: The e-Science Collaboration
Abstract:
The most used pavement for paved roads in the world is asphalt. It is therefore important that the asphalt is as durable as possible to avoid expensive repairs of the roads. One important factor of the durability of the road is the adherence between the stones and the bitumen that holds the stones together. The affinity is tested by the so called rolling bottle test, where one put stones covered in bitumen in a bottle with water and let it roll on a bottle rolling machine. After a while the degree of bitumen coverage is estimated. In this paper, a method to estimate the degree of bitumen coverage using image analysis has been developed instead of the manual estimation that is used today. The presented method works for all colors of the stones and is based on the fact that bitumen reflects light much better than raw stones. A turntable that is rotated somewhat between images is used together with a light source in shape of a quarter of a circle to get as much specular reflections as possible. Then the amount of detected reflections is used to estimate the degree of bitumen coverage. To be able to compare the result with something close to ground truth, the method has been evaluated on lighter stones and compared with a second image analysis method that works well for lighter stones, and the results are promising.
Keywords:
Machine Vision ; Bitumen Coverage ; Specular Reflections ; Segmentation ; Mathematics ; Computer Vision and Robotics (Autonomous Systems)
ISSN:
0263-2241
LUP-ID:
d9e0659d-cba2-43fb-a688-831e2d327446 | Link: https://lup.lub.lu.se/record/d9e0659d-cba2-43fb-a688-831e2d327446 | Statistics

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