Improved YOLOv7 Target Detection Algorithm Based on UAV Aerial Photography
Improved YOLOv7 Target Detection Algorithm Based on UAV Aerial Photography
- New search for: Zhen Bai
- New search for: Xinbiao Pei
- New search for: Zheng Qiao
- New search for: Guangxin Wu
- New search for: Yue Bai
- New search for: Zhen Bai
- New search for: Xinbiao Pei
- New search for: Zheng Qiao
- New search for: Guangxin Wu
- New search for: Yue Bai
With the rapid development of remote sensing technology, remote sensing target detection faces many problems; for example, there is still no good solution for small targets with complex backgrounds and simple features. In response to the above, we have added dynamic snake convolution (DSC) to YOLOv7. In addition, SPPFCSPC is used instead of the original spatial pyramid pooling structure; the original loss function was replaced with the EIoU loss function. This study was evaluated on UAV image data (VisDrone2019), which were compared with mainstream algorithms, and the experiments showed that this algorithm has a good average accuracy. Compared to the original algorithm, the mAP0.5 of the present algorithm is improved by 4.3%. Experiments proved that this algorithm outperforms other algorithms.
Document information
Improved YOLOv7 Target Detection Algorithm Based on UAV Aerial Photography
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Similar titles
MS-YOLOv7:YOLOv7 Based on Multi-Scale for Object Detection on UAV Aerial Photography
| DOAJ | 2023
Freeway vehicle detection on improved YOLOv7
| SPIE | 2023