- Title
- Deep learning applied to the semantic segmentation of tyre stockpiles
- Creator
- Barfknecht, Nicholas Christopher
- Subject
- Neural networks (Computer science)
- Date Issued
- 2018
- Date
- 2018
- Type
- Thesis
- Type
- Masters
- Type
- MSc
- Identifier
- http://hdl.handle.net/10948/23947
- Identifier
- vital:30647
- Description
- The global push for manufacturing which is environmentally sustainable has disrupted standard methods of waste tyre disposal. This push is further intensified by the health and safety risks discarded tyres pose to the surrounding population. Waste tyre recycling initiatives in South Africa are on the increase; however, there is still a growing number of undocumented tyre stockpiles developing throughout the country. The plans put in place to eradicate these tyre stockpiles have been met with collection, transport and storage logistical issues caused by the remoteness and distant locales. Eastwood (2016) aimed at optimising the logistics associated with collection, by estimating the number of visible tyres from images of tyre stockpiles. This research was limited by the need for manual segmentation of each tyre stockpile located within each image. This research proposes the use of semantic segmentation to automatically segment images of tyre stockpiles. An initial review of neural network, convolutional network and semantic segmentation literature resulted in the selection of Dilated Net as the semantic segmentation architecture for this research. Dilated Net builds upon the VGG-16 classification architecture to perform semantic segmentation. This resulted in classification experiments which were evaluated using precision, recall and f1-score. The results indicated that regardless of tyre stockpile image dimension, fairly accurate levels of classification accuracy can be attained. This was followed by semantic segmentation experiments which made use of intersection over union (IoU) and pixel accuracy to evaluate the effectiveness of Dilated Net on images of tyre stockpiles. The results indicated that accurate tyre stockpile segmentation regions can be obtained and that the trained model generalises well to unseen images.
- Format
- xi, 101 leaves
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela University
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Collections
NMU School of Computer Science, Mathematics, Physics and Statistics
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