  While general object detection with deep learning has achieved great success
in the past few years, the performance and efficiency of detecting small
objects are far from satisfactory. The most common and effective way to promote
small object detection is to use high-resolution images or feature maps.
However, both approaches induce costly computation since the computational cost
grows squarely as the size of images and features increases. To get the best of
two worlds, we propose QueryDet that uses a novel query mechanism to accelerate
the inference speed of feature-pyramid based object detectors. The pipeline
composes two steps: it first predicts the coarse locations of small objects on
low-resolution features and then computes the accurate detection results using
high-resolution features sparsely guided by those coarse positions. In this
way, we can not only harvest the benefit of high-resolution feature maps but
also avoid useless computation for the background area. On the popular COCO
dataset, the proposed method improves the detection mAP by 1.0 and mAP-small by
2.0, and the high-resolution inference speed is improved to 3.0x on average. On
VisDrone dataset, which contains more small objects, we create a new
state-of-the-art while gaining a 2.3x high-resolution acceleration on average.
Code is available at https://github.com/ChenhongyiYang/QueryDet-PyTorch.
