# Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection

arXiv: https://arxiv.org/abs/2407.20708 · HTML: https://mlsanity.com/paper/2407.20708/

## Key facts

- Cited by 65 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 30 Jul 2024.

## Built on by (most cited first, 30 of 65)

- [Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training](https://mlsanity.com/paper/2411.16061/) (2024)
- [Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset](https://mlsanity.com/paper/2412.06647/) (2024)
- [Spike2Former: Efficient Spiking Transformer for High-performance Image Segmentation](https://mlsanity.com/paper/2412.14587/) (2024)
- [Event-based Tiny Object Detection: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2506.23575/) (2025)
- [Rethinking Spiking Neural Networks from an Ensemble Learning Perspective](https://mlsanity.com/paper/2502.14218/) (2025)
- [Efficient 3D Recognition with Event-driven Spike Sparse Convolution](https://mlsanity.com/paper/2412.07360/) (2024)
- [SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks](https://mlsanity.com/paper/2503.08703/) (2025)
- [SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection](https://mlsanity.com/paper/2503.24389/) (2025)
- [SpikingBrain: Spiking Brain-inspired Large Models](https://mlsanity.com/paper/2509.05276/) (2025)
- [CM3AE: A Unified RGB Frame and Event-Voxel/-Frame Pre-training Framework](https://mlsanity.com/paper/2504.12576/) (2025)
- [SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and O(T) Complexity](https://mlsanity.com/paper/2505.10352/) (2025)
- [When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework](https://mlsanity.com/paper/2507.13659/) (2025)
- [PEOD: A Pixel-Aligned Event-RGB Benchmark for Object Detection under Challenging Conditions](https://mlsanity.com/paper/2511.08140/) (2025)
- [SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking](https://mlsanity.com/paper/2602.23963/) (2026)
- [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024)
- [SpiLiFormer: Enhancing Spiking Transformers with Lateral Inhibition](https://mlsanity.com/paper/2503.15986/) (2025)
- [Efficient Spiking Point Mamba for Point Cloud Analysis](https://mlsanity.com/paper/2504.14371/) (2025)
- [Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection](https://mlsanity.com/paper/2506.13440/) (2025)
- [SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba](https://mlsanity.com/paper/2510.04595/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Exploring the Potentials of Spiking Neural Networks for Image Deraining](https://mlsanity.com/paper/2512.02258/) (2025)
- [Efficient Spike-driven Transformer for High-performance Drone-View Geo-Localization](https://mlsanity.com/paper/2512.19365/) (2025)
- [SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference](https://mlsanity.com/paper/2604.22575/) (2026)
- [Decision SpikeFormer: Spike-Driven Transformer for Decision Making](https://mlsanity.com/paper/2504.03800/) (2025)
- [Dynamic Graph Induced Contour-aware Heat Conduction Network for Event-based Object Detection](https://mlsanity.com/paper/2505.12908/) (2025)
- [SVL: Empowering Spiking Neural Networks for Efficient 3D Open-World Understanding](https://mlsanity.com/paper/2505.17674/) (2025)
- [SNNSIR: A Simple Spiking Neural Network for Stereo Image Restoration](https://mlsanity.com/paper/2508.12271/) (2025)
- [Context-aware Sparse Spatiotemporal Learning for Event-based Vision](https://mlsanity.com/paper/2508.19806/) (2025)
- [Brain-Inspired Multimodal Spiking Neural Network for Image-Text Retrieval](https://mlsanity.com/paper/2603.26787/) (2026)
- [BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning](https://mlsanity.com/paper/2604.23165/) (2026)

## Abstract (arXiv)

Brain-inspired Spiking Neural Networks (SNNs) have bio-plausibility and low-power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are currently limited to simple classification tasks because of their poor performance. In this work, we focus on bridging the performance gap between ANNs and SNNs on object detection. Our design revolves around network architecture and spiking neuron. First, the overly complex module design causes spike degradation when the YOLO series is converted to the corresponding spiking version. We design a SpikeYOLO architecture to solve this problem by simplifying the vanilla YOLO and incorporating meta SNN blocks. Second, object detection is more sensitive to quantization errors in the conversion of membrane potentials into binary spikes by spiking neurons. To address this challenge, we design a new spiking neuron that activates Integer values during training while maintaining spike-driven by extending virtual timesteps during inference. The proposed method is validated on both static and neuromorphic object detection datasets. On the static COCO dataset, we obtain 66.2% mAP@50 and 48.9% mAP@50:95, which is +15.0% and +18.7% higher than the prior state-of-the-art SNN, respectively. On the neuromorphic Gen1 dataset, we achieve 67.2% mAP@50, which is +2.5% greater than the ANN with equivalent architecture, and the energy efficiency is improved by 5.7*. Code: https://github.com/BICLab/SpikeYOLO