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

arXiv: https://arxiv.org/abs/1803.09256 · HTML: https://mlsanity.com/paper/1803.09256/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2018.

## Built on by (most cited first, 18 of 18)

- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [Few-shot Object Counting and Detection](https://mlsanity.com/paper/2207.10988/) (2022)
- [Tracking Pedestrian Heads in Dense Crowd](https://mlsanity.com/paper/2103.13516/) (2021)
- [SIRNN: A Math Library for Secure RNN Inference](https://mlsanity.com/paper/2105.04236/) (2021)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry](https://mlsanity.com/paper/2407.04590/) (2024)
- [VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset](https://mlsanity.com/paper/2407.18245/) (2024)
- [MinUn: Accurate ML Inference on Microcontrollers](https://mlsanity.com/paper/2210.16556/) (2022)
- [Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks](https://mlsanity.com/paper/2212.11542/) (2022)
- [BPJDet: Extended Object Representation for Generic Body-Part Joint Detection](https://mlsanity.com/paper/2304.10765/) (2023)
- [TFCounter:Polishing Gems for Training-Free Object Counting](https://mlsanity.com/paper/2405.02301/) (2024)
- [Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark](https://mlsanity.com/paper/2604.02773/) (2026)
- [Perception Reinforcement Using Auxiliary Learning Feature Fusion: A Modified Yolov8 for Head Detection](https://mlsanity.com/paper/2310.09492/) (2023)
- [Online Pseudo-Label Unified Object Detection for Multiple Datasets Training](https://mlsanity.com/paper/2410.15569/) (2024)
- [RPEE-HEADS: A Novel Benchmark for Pedestrian Head Detection in Crowd Videos](https://mlsanity.com/paper/2411.18164/) (2024)
- [Head Anchor Enhanced Detection and Association for Crowded Pedestrian Tracking](https://mlsanity.com/paper/2508.05514/) (2025)
- [OpenConstruction: A Systematic Synthesis of Open Visual Datasets for Data-Centric Artificial Intelligence in Construction Monitoring](https://mlsanity.com/paper/2508.11482/) (2025)
- [Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering](https://mlsanity.com/paper/2603.18166/) (2026)

## Abstract (arXiv)

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.