Recently, disaster scene description and indexing challenges attract the attention of researchers. In this dissertation, we solve a disaster-related multi-labeling task using a newly developed Low Altitude Disaster Imagery dataset. In the first task, we realize video content by selecting a set of summary key frames to represent the video sequence. Through inter-frame differences, the key frames are generated. The key frame extraction of disaster-related video clips is a powerful tool that can efficiently convert video data into image-level data, reduce the requirements for the extraction environment and improve the applicable environment. In the second, we propose a novel application of using deep learning methods on low altitude disaster video feature recognition. Supervised learning-based deep-learning approaches are effective in disaster-related features recognition via foreground object detection and background classification. Performed dataset validation, our model generalized well and improved performance by optimizing the YOLOv3 model and combining it with Resnet50. The comprehensive models showed more efficient and effective than those in prior published works. In the third task, we optimize the whole scene labeling classification by pruning the lightweight model MobileNetV3, which shows superior generalizability and can disaster features recognition from a disaster-related dataset be accomplished efficiently to assist disaster recovery.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1873789 |
| Date | 12 1900 |
| Creators | Li, Yuan |
| Contributors | Buckles, Bill P., 1942-, Blanco, Eduardo, Namuduri, Kamesh, Huang, Yan, 1974- |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | ix, 112 pages : illustrations (some color), Text |
| Rights | Public, Li, Yuan, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
Page generated in 0.0015 seconds