Object detection on remote sensing images using deep learning: an improved single shot multibox detector method

Kun Zhao; Xiaoxi Ren; Zhenzhen Kong; Min Liu

doi:10.1117/1.JEI.28.3.033026

10 June 2019 Object detection on remote sensing images using deep learning: an improved single shot multibox detector method

Kun Zhao, Xiaoxi Ren, Zhenzhen Kong, Min Liu

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 3, 033026 (June 2019). https://doi.org/10.1117/1.JEI.28.3.033026

Abstract

Remote sensing images recognition technology has great significance in many aspects, such as military navigation and environmental monitoring. We propose an improved single shot multibox detector approach by combining some strategies, including upsampling, focal loss, and proper calibration of key parameters. Comprehensive experiments on three remote sensing images datasets have demonstrated the effectiveness of the proposed approach in benchmarking with several state-of-the-art object detection methods.

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Kun Zhao, Xiaoxi Ren, Zhenzhen Kong, and Min Liu "Object detection on remote sensing images using deep learning: an improved single shot multibox detector method," Journal of Electronic Imaging 28(3), 033026 (10 June 2019). https://doi.org/10.1117/1.JEI.28.3.033026

Received: 18 December 2018; Accepted: 2 April 2019; Published: 10 June 2019

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