教授
发布时间：2021-11-02
点击次数：
| 影响因子： | 3.732 |
| DOI码： | 10.1007/s00500-021-06407-8 |
| 所属单位： | 长江大学计算机科学学院 |
| 发表刊物： | Soft Computing |
| 刊物所在地： | Cham, Switzerland |
| 项目来源： | 中国博士后科学基金2019TQ0291；航空科学基金2018ZCZ2002；湖北省自然科学基金2019CFB376；产学研创新基金2019ITA03004；荆州科技计划2018024 |
| 关键字： | Small object detection · Object detection · Unmanned aerial vehicle (UAV) · Attention |
| 摘要： | The object detection algorithm is mainly focused on detection in general scenarios, when the same algorithm is applied to drone-captured scenes, and the detection performance of the algorithm will be significantly reduced. Our research found that small objects are the main reason for this phenomenon. In order to verify this finding, we choose the yolov5 model and propose four methods to improve the detection precision of small object based on it. At the same time, considering that the model needs to be small in size, speed fast, low cost and easy to deploy in actual application, therefore, when designing these four methods, we also fully consider the impact of these methods on the detection speed. The model integrating all the improved methods not only greatly improves the detection precision, but also effectively reduces the loss of detection speed. Finally, based on VisDrone-2020, the mAP of our model is increased from 12.7 to 37.66%, and the detection speed is up to 55FPS. It is to outperform the earlier state of the art in detection speed and promote the progress of object detection algorithms on drone platforms. |
| 合写作者： | Chenfan Sun, Maocai Wang, Jinhui She, Yangyang Zhang, Zhiliang Zhang, Yong Sun |
| 第一作者： | Wei Zhan |
| 论文类型： | 期刊论文 |
| 论文编号： | 20260830-015 |
| 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： | 26 |
| 期号： | 1 |
| 页面范围： | 361-373 |
| 字数： | 6362 |
| ISSN号： | 1432-7643; 1433-7479 |
| 发表时间： | 2022-01-01 |
