Rotation invariant bin detection and solid waste level classification
In this paper, a solid waste bin detection and waste level classification system that is rotation invariant is presented. First, possible locations and orientations of the bin are detected using Hough line detection. Then cross correlation is calculated to differentiate the true bin position and ori...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2015
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/14033/1/Rotation_invariant_bin_detection_and_solid_waste_level_classification.pdf http://eprints.um.edu.my/14033/ http://www.sciencedirect.com/science/article/pii/S0263224114006253 |
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| Summary: | In this paper, a solid waste bin detection and waste level classification system that is rotation invariant is presented. First, possible locations and orientations of the bin are detected using Hough line detection. Then cross correlation is calculated to differentiate the true bin position and orientation from those of other similar objects. Next, features are extracted from the inside of the bin area and together with detected bin corners they are used to determine the bin's waste level. A few features are also obtained from the outside of the bin area to check whether there is rubbish littered outside the bin. The proposed system was tested on shifted, rotated and unrotated bin images containing different level of waste. In the experiment, bin detection was treated separately from waste level classification. For bin detection, if 95 of the opening area is captured then the bin is considered detected correctly. The waste level classification is only considered for the correctly detected bins where the waste level is classified as empty, partially full or full. The system also checks the presence of rubbish outside the bin. In training, only images containing unrotated bin were used while in testing images containing both unrotated and rotated bin were used in equal number. The system achieves an average bin detection rate of 97.5 and waste level classification rate of 99.4 despite variations in the bin's location, rotation and content. It is also robust against occlusion of the bin opening by large objects and confusion from square objects littered outside the bin. Its low average execution time suggests that the proposed method is suitable for real-time implementation. (C) 2014 Elsevier Ltd. All rights reserved. |
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