Abstract
In this paper, we present our solution of the 1st Tiny Object Detection (TOD) Challenge. The purpose of the challenge is to detect tiny person objects (2–20 pixels) in large-scale images. Due to the extreme small object size and low signal-to-noise ratio, the detection of tiny objects is much more challenging than objects in other datasets such as COCO and CityPersons. Based on Faster R-CNN, we explore some effective and general methods to improve the detection performance of tiny objects. Since the model architectures will not be changed, these methods are easy to implement. Accordingly, we obtain the 2nd place with the \(A P_{50}^{{\text {tiny}}}\) score of 71.53 in the challenge.
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Acknowledgement
This paper was financial supported partially by Special Funds of the Jiangsu Provincial Key Research and Development Projects (grant No. BE2019612) and Jiangsu Provincial Cadre Health Research Projects (grant No. BJ17006).
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Gao, C., Tang, W., Jin, L., Jun, Y. (2020). Exploring Effective Methods to Improve the Performance of Tiny Object Detection. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12539. Springer, Cham. https://doi.org/10.1007/978-3-030-68238-5_25
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DOI: https://doi.org/10.1007/978-3-030-68238-5_25
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