# ThunderNet: Towards Real-time Generic Object Detection

arXiv: https://arxiv.org/abs/1903.11752 · HTML: https://mlsanity.com/paper/1903.11752/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 30 of 42)

- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
- [Distilling Object Detectors via Decoupled Features](https://mlsanity.com/paper/2103.14475/) (2021)
- [Adaptive Class Suppression Loss for Long-Tail Object Detection](https://mlsanity.com/paper/2104.00885/) (2021)
- [Residual Attention: A Simple but Effective Method for Multi-Label Recognition](https://mlsanity.com/paper/2108.02456/) (2021)
- [Q-VLM: Post-training Quantization for Large Vision-Language Models](https://mlsanity.com/paper/2410.08119/) (2024)
- [Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images](https://mlsanity.com/paper/2303.14488/) (2023)
- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [A Review on Deep Learning in UAV Remote Sensing](https://mlsanity.com/paper/2101.10861/) (2021)
- [A Survey of Deep Learning Techniques for Weed Detection from Images](https://mlsanity.com/paper/2103.01415/) (2021)
- [Multi-Source Domain Adaptation for Object Detection](https://mlsanity.com/paper/2106.15793/) (2021)
- [Prediction-Guided Distillation for Dense Object Detection](https://mlsanity.com/paper/2203.05469/) (2022)
- [YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs](https://mlsanity.com/paper/2110.13713/) (2021)
- [Deep Network Approximation in Terms of Intrinsic Parameters](https://mlsanity.com/paper/2111.07964/) (2021)
- [A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey](https://mlsanity.com/paper/2208.10895/) (2022)
- [Generalizable Mixed-Precision Quantization via Attribution Rank Preservation](https://mlsanity.com/paper/2108.02720/) (2021)
- [CE-FPN: Enhancing Channel Information for Object Detection](https://mlsanity.com/paper/2103.10643/) (2021)
- [Few-Shot Video Object Detection](https://mlsanity.com/paper/2104.14805/) (2021)
- [Learning Calibrated-Guidance for Object Detection in Aerial Images](https://mlsanity.com/paper/2103.11399/) (2021)
- [Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation](https://mlsanity.com/paper/2310.18676/) (2023)
- [An Empirical Study of Adder Neural Networks for Object Detection](https://mlsanity.com/paper/2112.13608/) (2021)
- [DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention](https://mlsanity.com/paper/2209.13933/) (2022)
- [MobileDenseNet: A new approach to object detection on mobile devices](https://mlsanity.com/paper/2207.11031/) (2022)
- [Knowledge Distillation for Object Detection: from generic to remote sensing datasets](https://mlsanity.com/paper/2307.09264/) (2023)
- [Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data](https://mlsanity.com/paper/2311.04040/) (2023)
- [From large-eddy simulations to deep learning: A U-net model for fast urban canopy flow predictions](https://mlsanity.com/paper/2507.06533/) (2025)
- [LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2209.07709/) (2022)
- [TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors](https://mlsanity.com/paper/2304.03428/) (2023)
- [Self-Training and Multi-Task Learning for Limited Data: Evaluation Study on Object Detection](https://mlsanity.com/paper/2309.06288/) (2023)
- [Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection](https://mlsanity.com/paper/2108.13775/) (2021)
- [DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention](https://mlsanity.com/paper/2111.00500/) (2021)

## Abstract (arXiv)

Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight two-stage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Our code and models are available at urlhttps://github.com/qinzheng93/ThunderNet.