In recent years, deep learning based computer vision technology has developed rapidly. This is not only due to the improvement of computing power, but also due to the emergence of high-quality datasets. The combination of object detectors and drones has great potential in the field of rescue and disaster relief. We created an image dataset specifically for vision applications on drone platforms. The dataset contains 5000 images, and each image is carefully labeled according to the PASCAL VOC standard. This specific dataset will be very important for developing deep learning algorithms for drone applications. In object detection models, loss function plays a vital role. Considering the uneven distribution of large and small objects in the dataset, we propose adjustment coefficients based on the frequencies of objects of different sizes to adjust the loss function, and finally improve the accuracy of the model.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1808467 |
| Date | 05 1900 |
| Creators | Qi, Yunlong |
| Contributors | Fu, Shengli, Li, Xinrong, Sun, Hua |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | ix, 57 pages, Text |
| Rights | Public, Qi, Yunlong, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
Page generated in 0.0255 seconds