University of Twente Student Theses
Development of Deep Learning Model for Building Opening Detection as the Application of Unmanned Aerial Vehicle (UAV) in Urban Search and Rescue Mission
Surojaya, Ali (2023) Development of Deep Learning Model for Building Opening Detection as the Application of Unmanned Aerial Vehicle (UAV) in Urban Search and Rescue Mission.
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| Abstract: | Automatic Unmanned Aerial Vehicle (UAV) is a promising technology to minimise human involvement in dangerous activities like urban search and rescue missions (USAR). For this purpose, the ability of a UAV that can map and locate the openings in the damaged building is needed to be able to transit between outdoor and indoor environments. This study focuses on developing a deep learning model for real-time damaged building opening detection by comparing the performance of single and multi-task learning-based detectors. This study consists of four phases: dataset development, single-task network training, multi-task network design, and model performance testing and comparison. This work successfully provides a novel damaged building opening dataset containing images and mask annotations. The deep learning-based detector used in this study is based on YOLOv5 as the most stable current state-of-the-art of real-time object detection model. First, this study compares the different versions of YOLOv5 (i.e. small, medium, and large) to perform damaged building opening detections. Second, multi-task learning YOLOv5 is trained on the same dataset and compared with the single-task detector. This study found that multi-task learning (MTL) based YOLOv5 can improve detection performance by combining detection and segmentation losses. The YOLOv5s-MTL trained on the damaged building opening dataset obtained 0.648 mAP, an increase of 0.167 from the single-task-based network. Regarding the inference speed, the YOLOv5s-MTL can run inference in 73 frames per second on the tested platform. Overall, this study provides a novelty by contributing to developing an initial damaged building opening dataset and detection model. It also demonstrates that a multi-task learning method can improve detection accuracy. |
| Item Type: | Essay (Master) |
| Faculty: | ITC: Faculty of Geo-information Science and Earth Observation |
| Subject: | 54 computer science |
| Programme: | Geoinformation Science and Earth Observation MSc (75014) |
| Link to this item: | https://purl.utwente.nl/essays/95692 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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