Abstract
Small object detection is a challenging computer vision problem due to their low feature representation in the images and factors such as occlusions and noise. In images captured from a camera mounted on an unmanned aerial vehicle (UAV), objects are usually acquired in small sizes depending on the UAV flight altitude. The state-of-the-art object detectors often have lower detection accuracy with small objects. New approaches of combining features at multi-levels in the network helps in improving the object detection performance. In this paper, we propose a multi-scale approach of low-level feature combinations with deconvolutional modules on a single shot multibox detection (SSD) object detector to improve the small object detection in images acquired from a UAV. The proposed SSD based architecture is evaluated on UAV datasets to compare its performance with the state-of-the-art detectors.
The research leading to these results have received funding from the Department for Digital, Culture, Media & Sports (DCMS), United Kingdom, under its 5G trials and testbeds program.
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Razaak, M., Kerdegari, H., Argyriou, V., Remagnino, P. (2019). Multi-scale Feature Fused Single Shot Detector for Small Object Detection in UAV Images. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_71
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DOI: https://doi.org/10.1007/978-3-030-34995-0_71
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