Remote Sensing Image Object Detection Method Based On Improved YOLOv3

International Journal of Advanced Network, Monitoring, and Controls

article

Zhiyuan Lu¹  Bailin Liu¹ 

[1] School of Computer Science and Engineering Xi'an Technological University Xi’an

关键词: Remote Sensing Image;    Object Detection;    Yolov3;    DOIR Dataset;   

DOI  :  10.2478/ijanmc-2022-0031

学科分类：社会科学、人文和艺术（综合）

来源: Asociación Regional De Diálisis Y Trasplantes Renales

【 摘 要 】

In order to solve the problem that irregular targets and dense targets are difficult to be detected in optical remote sensing images, this paper improved the YOLOV3 model Firstly, in order to further combine the feature information of different scales, the PaNet structure is introduced into the FPN part of the original YOLOv3, and the obtained effective feature layer is continued to be extracted for a round of feature. The feature is not only up-sampled to achieve feature fusion, but also down-sampled again to achieve enhanced feature fusion SimOTA method is introduced to dynamically match positive samples and set different positive sample numbers for different targets, which not only improves the speed of the algorithm, but also reduces the extra hyperparameters Experimental verification using richer DOIR data sets shows that the detection ability of the improved algorithm is significantly improved. Compared with the original YOLOv3, its mAP improves by 15.1 points, among which the detection accuracy of dense small targets is improved the most.

【 授权许可】

CC BY-NC-ND