# Detecting Heads using Feature Refine Net and Cascaded Multi-Scale Architecture

## TL;DR

This paper introduces a novel head detection method combining Feature Refine Net and cascaded multi-scale architecture, effectively detecting small heads in indoor scenes, and presents a new large dataset for head detection.

## Contribution

The paper proposes a new Feature Refine Net and cascaded multi-scale architecture for improved head detection, especially small heads, and introduces the SCUT-HEAD dataset.

## Key findings

- Achieved state-of-the-art performance on SCUT-HEAD dataset.
- Effectively detects small heads in indoor scenes.
- Introduced a large annotated dataset for head detection.

## Abstract

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.