Visual object detection has achieved unprecedented ad-vance with the rise of
deep convolutional neural networks.However, detecting tiny objects (for example
tiny per-sons less than 20 pixels) in large-scale images remainsnot well
investigated. The extremely small objects raisea grand challenge about feature
representation while themassive and complex backgrounds aggregate the risk
offalse alarms. In this paper, we introduce a new benchmark,referred to as
TinyPerson, opening up a promising directionfor tiny object detection in a long
distance and with mas-sive backgrounds. We experimentally find that the scale
mis-match between the dataset for network pre-training and thedataset for
detector learning could deteriorate the featurerepresentation and the
detectors. Accordingly, we proposea simple yet effective Scale Match approach
to align theobject scales between the two datasets for favorable tiny-object
representation. Experiments show the significantperformance gain of our
proposed approach over state-of-the-art detectors, and the challenging aspects
of TinyPersonrelated to real-world scenarios. The TinyPerson benchmarkand the
code for our approach will be publicly
available(this https URL).(Attention: evaluation
rules of AP have updated in benchmark after this paper accepted, So this paper
use old rules. we will keep old rules of AP in benchmark, but we recommand the
new and we will use the new in latter research.)

本研究提出了一个名为 TinyPerson 的新基准，针对大规模图像中的微小对象（例如少于 20 像素的微小人物），引入了远距离和大背景下微小目标检测的有前途的方向，并且通过采用简单而有效的比例匹配方法，在避免特征表示和检测器退化的同时获得了显著的性能提升，并且进一步展示了 TinyPerson 与真实世界场景相关的挑战。