教授
发布时间：2020-10-13
点击次数：
| 影响因子： | 1.025 |
| DOI码： | 10.1155/2020/4013647 |
| 所属单位： | 长江大学计算机科学学院 |
| 发表刊物： | Mathematical Problems in Engineering |
| 刊物所在地： | London, UK |
| 摘要： | The aim of this research is to show the implementation of object detection on drone videos using TensorFlow object detection API. The function of the research is the recognition effect and performance of the popular target detection algorithm and feature extractor for recognizing people, trees, cars, and buildings from real-world video frames taken by drones. The study found that using different target detection algorithms on the “normal” image (an ordinary camera) has different performance effects on the number of instances, detection accuracy, and performance consumption of the target and the application of the algorithm to the image data acquired by the drone is different. Object detection is a key part of the realization of any robot’s complete autonomy, while unmanned aerial vehicles (UAVs) are a very active area of this field. In order to explore the performance of the most advanced target detection algorithm in the image data captured by UAV, we have done a lot of experiments to solve our functional problems and compared two different types of representative of the most advanced convolution target detection systems, such as SSD and Faster R-CNN, with MobileNet, GoogleNet/Inception, and ResNet50 base feature extractors. |
| 合写作者： | Jinhiu She, Yangyang Zhang |
| 第一作者： | Chenfan Sun |
| 论文类型： | 期刊论文 |
| 通讯作者： | Wei Zhan |
| 论文编号： | 20260830-019 |
| 学科门类： | 工学 |
| 一级学科： | 计算机科学与技术 |
| 文献类型： | 期刊 |
| 卷号： | 2020 |
| 期号： | 1 |
| 页面范围： | 4013647 |
| ISSN号： | 1563-5147; 1024-123X |
| 发表时间： | 2020-10-13 |
