Improved YOLOv7 Target Detection Algorithm Based on UAV Aerial Photography
Author:
Bai Zhen12, Pei Xinbiao12, Qiao Zheng12, Wu Guangxin12, Bai Yue12
Affiliation:
1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China 2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract
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.
Funder
Innovation Guidance Fund Project of Light Power Innovation Research Institute, Chinese Academy of Sciences National Key R&D Program
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2 articles.
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