Multi-scale Feature Fused Single Shot Detector for Small Object Detection in UAV Images

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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.

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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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11754 LNCS, pp. 778–786). Springer. https://doi.org/10.1007/978-3-030-34995-0_71

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