Semantic segmentation of bridge inspection images for damage assessment | |
| Author | Teera Laiteerapong |
| Call Number | AIT Thesis no.CS-19-02 |
| Subject(s) | Bridges--Maintenance and repair Structural analysis (Engineering) Image processing--Digital techniques |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. CS-19-02 |
| Abstract | Bridge maintenance required monitoring. Otherwise, a severe accident could happen. In general, the bridge maintenance required an inspector to go to the bridge, take a photo, and collect them at the government office. The procedures require an inspector to determine the type of damage corresponding to the photos. This research work attempts to assist the inspector by automatically labeling the damage type on the particular area in the image. The approaches begin with fully convolutional network as a baseline mode, the state-of-the-art model Mask R-CNN, and hyperparameters fine-tuning. The dataset contains two classes of the bridge damage on the deck area. The first one is delamination, and the second one is rebar exposure. The exist of rebar exposure can be interpret as a severe case. Therefore, false negative is as crucial as false positive in this kind of dataset. This dataset contains three experts labelled. However, there is much disagreement among their labels. The mixture of experts using non-maximum suppression helps obtain more recall. Lastly, with all the damage label information, the government can plan the bridge maintenance more effectively. |
| Year | 2019 |
| Corresponding Series Added Entry | Asian Institute of Technology. : no. CS-19-02 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Dailey, Matthew N.; |
| Examination Committee(s) | Mongkol Ekpanyapong;Kan Ouivirach; |
| Scholarship Donor(s) | Royal Thai Government Fellowship; |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2019 |