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Auxiliary Bounding Box Regression for Object Detection in Optical Remote Sensing Imagery

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Abstract

Object detection in optical remote sensing imagery is being explored to deal with arbitrary orientations and complex appearance which is still a major issue in recent years. To perceive a better solution to the addressed problem, the post-processing of bounding boxes (BBs) has been evaluated and discussed for the applications of object detection. In this paper, the proposed method has divided into two stages; the first stage is based on thresholding of BBs with respect to the confidence values and the second stage is based on the area-based BB regression (BBR). In BBR, the area of each BB was estimated then the oversized and undersized BBs were removed with respect to the size of objects which are being detected. The widely known region-based approaches RCNN, Fast-RCNN and Faster-RCNN are used for evaluation and comparative analysis validates the proposed framework. The results show that the proposed post-processing is very effective for each kind of region-based detector.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grants 61471148.

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Authors and Affiliations

  1. Department of Computer Science, ILMA University, Karachi, Pakistan

    Shahid Karim

  2. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Shahid Karim, Ye Zhang & Shoulin Yin

  3. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Irfana Bibi

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  1. Shahid Karim
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  2. Ye Zhang
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  4. Irfana Bibi
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Correspondence to Shahid Karim.

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Karim, S., Zhang, Y., Yin, S. et al. Auxiliary Bounding Box Regression for Object Detection in Optical Remote Sensing Imagery. Sens Imaging 22, 5 (2021). https://doi.org/10.1007/s11220-020-00319-x

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  • DOI: https://doi.org/10.1007/s11220-020-00319-x

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