  This paper presents a method that can accurately detect heads especially
small heads under the indoor scene. To achieve this, we propose a novel method,
Feature Refine Net (FRN), and a cascaded multi-scale architecture. FRN exploits
the multi-scale hierarchical features created by deep convolutional neural
networks. The proposed channel weighting method enables FRN to make use of
features alternatively and effectively. To improve the performance of small
head detection, we propose a cascaded multi-scale architecture which has two
detectors. One called global detector is responsible for detecting large
objects and acquiring the global distribution information. The other called
local detector is designed for small objects detection and makes use of the
information provided by global detector. Due to the lack of head detection
datasets, we have collected and labeled a new large dataset named SCUT-HEAD
which includes 4405 images with 111251 heads annotated. Experiments show that
our method has achieved state-of-the-art performance on SCUT-HEAD.
