This study presents an exploration of several machine learning and image processing theories, as well as a literature review of several previous works on concrete crack detection systems. Through the literature review a system is selected and implemented with the Öresund bridge photograph collection. The selected system is a Convolutional Neural Network (CNN) using cropped (256x256x) images for input. The CNN has a total of 13 layers that were implemented as described in the paper. All parts of the implementation such as cropping, code, and testing are described in detail. This study found a final accuracy rate of 77% for the trained net. This is combined with a sliding window technique for handling larger images. An exploration of reasons for this accuracy rate is done at the end of the paper.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hkr-20854 |
| Date | January 2020 |
| Creators | de Redelijkheid, Martijn, Kokoneshi, Kristian |
| Publisher | Högskolan Kristianstad, Avdelningen för datavetenskap, Högskolan Kristianstad, Fakulteten för ekonomi, Högskolan Kristianstad, Avdelningen för datavetenskap, Högskolan Kristianstad, Fakulteten för ekonomi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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