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Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery

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journal contribution
posted on 14.09.2021, 08:45 by P hamsolmoali, M Zareapoor, J Chanussot, Huiyu Zhou, J Yang
Detection of object is extremely important in various aerial vision-based applications. Over the last few years, the methods based on convolution neural networks have made substantial progress. However, because of the large variety of object scales, densities and arbitrary orientations, the current detectors struggle with extraction of semantically strong feature for small-scale objects by predefined convolution kernel. To address this problem, we propose the rotation equivariant feature image pyramid network (REFIPN), an image pyramid network based on rotation equivariance convolution. The proposed model adopts single-shot detector in parallel with a light-weight image pyramid module to extract representative feature and generate region of interests in an optimization approach. The proposed network extracts features in a wide range of scales and orientations by using novel convolution filters. These features are used to generate vector fields and determine the weight and angle of the highest scoring orientation for all spatial locations on an image. By this approach, the performance for small-sized object detection is enhanced without sacrificing the performance for large-sized object detection. The performance of the proposed model is validated on two commonly used aerial benchmarks and the results show our propose model can achieve state-of-the-art performance with satisfactory efficiency.

Funding

NSFC, China (No: 61876107,U1803261)and Committee of Science and Technology, Shanghai, China (No.19510711200).

History

Citation

IEEE Transactions on Geoscience and Remote Sensing, In press

Author affiliation

School of Informatics

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Geoscience and Remote Sensing

Publisher

Institute of Electrical and Electronics Engineers

issn

0196-2892

Acceptance date

05/09/2021

Copyright date

2021

Available date

14/09/2021

Publisher DOI

Language

en