Mosque Building Detection Using Deep Convolutional Neural Network
| dc.contributor.advisor | Belay, Ayalew (PhD) | |
| dc.contributor.author | Ergete, Samrawit | |
| dc.date.accessioned | 2021-01-15T07:51:43Z | |
| dc.date.accessioned | 2023-11-29T04:06:13Z | |
| dc.date.available | 2021-01-15T07:51:43Z | |
| dc.date.available | 2023-11-29T04:06:13Z | |
| dc.date.issued | 2020-10-10 | |
| dc.description.abstract | Object detection is a computer technology related to computer vision and image processing that detects and defines objects such as humans, buildings and cars from images and videos. Object detection is breaking into a wide range of industries, with use cases ranging from personal security to productivity in the workplace. Facial recognition or face detection is one of an object detection examples, which can be utilized as a security measure to let only certain people into a classified area of building. It can also be used within a visual search engine to help consumers find a specific item they’re on the hunt for. In this work we propose detection system start from collecting and preparing data to detecting mosque building by using deep convolutional neural network (DCNN). Mosque building detection is done using Faster RCNN model. Faster RCNN is trained on 1848 dataset collected from different websites and by directly taking pictures and splinted into 90% for training and 10% for testing. Experimental results have proved the efficiency of the proposed technique, where the accuracy of the proposed scheme has achieved mAP of 0.70. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/24700 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Mosque Building | en_US |
| dc.subject | Image Processing | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Detection | en_US |
| dc.subject | Faster Rcnn | en_US |
| dc.title | Mosque Building Detection Using Deep Convolutional Neural Network | en_US |
| dc.type | Thesis | en_US |