Aircraft Recognition from Remote Sensing Images Based on Machine Vision


Lu Chen, Liming Zhou, Jinming Liu, Journal of Information Processing Systems Vol. 16, No. 4, pp. 795-808, Aug. 2020  

10.3745/JIPS.02.0136
Keywords: Aircraft Recognition, Inception Module, Remote Sensing Images
Fulltext:

Abstract

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.


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Cite this article
[APA Style]
Lu Chen, Liming Zhou, & Jinming Liu (2020). Aircraft Recognition from Remote Sensing Images Based on Machine Vision. Journal of Information Processing Systems, 16(4), 795-808. DOI: 10.3745/JIPS.02.0136.

[IEEE Style]
L. Chen, L. Zhou and J. Liu, "Aircraft Recognition from Remote Sensing Images Based on Machine Vision," Journal of Information Processing Systems, vol. 16, no. 4, pp. 795-808, 2020. DOI: 10.3745/JIPS.02.0136.

[ACM Style]
Lu Chen, Liming Zhou, and Jinming Liu. 2020. Aircraft Recognition from Remote Sensing Images Based on Machine Vision. Journal of Information Processing Systems, 16, 4, (2020), 795-808. DOI: 10.3745/JIPS.02.0136.