<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Remote Sensing Image Object Detection Method Based On Improved YOLOv3

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Remote Sensing Image Object Detection Method Based On Improved YOLOv3"}

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...

Remote Sensing Image Object Detection Method Based On Improved YOLOv3

Online veröffentlicht: 26. Mai 2023

Seitenbereich: 1 - 8

DOI: https://doi.org/10.2478/ijanmc-2022-0031

Schlüsselwörter
Remote Sensing Image, Object Detection, Yolov3, DOIR Dataset

© 2022 Zhiyuan Lu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Remote Sensing Image Object Detection Method Based On Improved YOLOv3

Online veröffentlicht: 26. Mai 2023

Seitenbereich: 1 - 8

DOI: https://doi.org/10.2478/ijanmc-2022-0031

SchlüsselwörterRemote Sensing Image, Object Detection, Yolov3, DOIR Dataset

© 2022 Zhiyuan Lu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Remote Sensing Image, Object Detection, Yolov3, DOIR Dataset