# HS-FPN: High Frequency and Spatial Perception FPN for Tiny Object Detection

arXiv: https://arxiv.org/abs/2412.10116 · HTML: https://mlsanity.com/paper/2412.10116/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://mlsanity.com/paper/2006.02334/) (2020)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [FcaNet: Frequency Channel Attention Networks](https://mlsanity.com/paper/2012.11879/) (2020)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
- [Grid R-CNN](https://mlsanity.com/paper/1811.12030/) (2018)
- [Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution](https://mlsanity.com/paper/1909.06720/) (2019)
- [RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection](https://mlsanity.com/paper/2208.08738/) (2022)
- [CE-FPN: Enhancing Channel Information for Object Detection](https://mlsanity.com/paper/2103.10643/) (2021)

## Built on by (most cited first, 22 of 22)

- [Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective](https://mlsanity.com/paper/2508.06878/) (2025)
- [AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection](https://mlsanity.com/paper/2505.15184/) (2025)
- [D3R-DETR: DETR with Dual-Domain Density Refinement for Tiny Object Detection in Aerial Images](https://mlsanity.com/paper/2601.02747/) (2026)
- [WhisperNet: A Scalable Solution for Bandwidth-Efficient Collaboration](https://mlsanity.com/paper/2603.01708/) (2026)
- [MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection](https://mlsanity.com/paper/2506.12697/) (2025)
- [Power Battery Detection](https://mlsanity.com/paper/2508.07797/) (2025)
- [PRNet: Original Information Is All You Have](https://mlsanity.com/paper/2510.09531/) (2025)
- [Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark](https://mlsanity.com/paper/2604.02773/) (2026)
- [CP-WSP: A Declarative CP-SAT Framework for Configurable Multi-Constraint Workforce Scheduling](https://mlsanity.com/paper/2607.05177/) (2026)
- [Ice Hockey Puck Localization Using Contextual Cues](https://mlsanity.com/paper/2506.04365/) (2025)
- [KAN-FPN-Stem:A KAN-Enhanced Feature Pyramid Stem for Boosting ViT-based Pose Estimation](https://mlsanity.com/paper/2512.22228/) (2025)
- [Learning Where to Focus: Density-Driven Guidance for Detecting Dense Tiny Objects](https://mlsanity.com/paper/2512.22949/) (2025)
- [FGAA-FPN: Foreground-Guided Angle-Aware Feature Pyramid Network for Oriented Object Detection](https://mlsanity.com/paper/2602.10710/) (2026)
- [AWPD: Frequency Shield Network for Agnostic Watermark Presence Detection](https://mlsanity.com/paper/2603.06723/) (2026)
- [Anatomy of a failure: When, how, and why deep vision fails in scientific domains](https://mlsanity.com/paper/2605.04231/) (2026)
- [Denoising-Enhanced Coarse-to-Fine Infrared Small Target Detection with Attention Prior-Guided Knowledge Distillation](https://mlsanity.com/paper/2606.21956/) (2026)
- [Adaptive Spectrum-Aware Feature Disentangled Network for Small Object Detection](https://mlsanity.com/paper/2606.29029/) (2026)
- [FR-DETR: Frequency and Recurrent Feature Refinement for Robust Object Detection under Adverse Weather](https://mlsanity.com/paper/2606.30471/) (2026)
- [DyFrDet: Towards Accurate Small Object Detection via Dynamic Frequency Suppression with Label Disambiguation](https://mlsanity.com/paper/2608.02495/) (2026)
- [RailSyn: Diagnosis-Guided Image Generation for Traceable Data Completion in Railway Foreign Object Detection](https://mlsanity.com/paper/2608.30709/) (2026)
- [HDMamba-YOLO: Efficient State-Space Perception and Local Spatial Reconstruction for UAV Small Object](https://mlsanity.com/paper/2609.23061/) (2026)
- [FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection](https://mlsanity.com/paper/2609.29125/) (2026)

## Abstract (arXiv)

The introduction of Feature Pyramid Network (FPN) has significantly improved object detection performance. However, substantial challenges remain in detecting tiny objects, as their features occupy only a very small proportion of the feature maps. Although FPN integrates multi-scale features, it does not directly enhance or enrich the features of tiny objects. Furthermore, FPN lacks spatial perception ability. To address these issues, we propose a novel High Frequency and Spatial Perception Feature Pyramid Network (HS-FPN) with two innovative modules. First, we designed a high frequency perception module (HFP) that generates high frequency responses through high pass filters. These high frequency responses are used as mask weights from both spatial and channel perspectives to enrich and highlight the features of tiny objects in the original feature maps. Second, we developed a spatial dependency perception module (SDP) to capture the spatial dependencies that FPN lacks. Our experiments demonstrate that detectors based on HS-FPN exhibit competitive advantages over state-of-the-art models on the AI-TOD dataset for tiny object detection.