S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion

Li CHEN; Fan ZHANG; Guangwei XIE; Yanzhao GAO; Xiaofeng QI; Mingqian SUN

doi:10.1631/FITEE.2400594

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S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion

Regular Papers | Updated：2025-06-04

- S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion
- S3Det：一种基于人工—脉冲神经网络转换的遥感影像目标快速检测模型
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 5, Pages: 713-727(2025)
- Affiliations：
  
  1.National Digital Switching System Engineering & Technological R&D Center, Zhengzhou 450003, China
  2.Computation and Artificial Intelligence Innovative College, Fudan University, Shanghai 201203, China
  3.School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
- Author bio：
  
  ‡ Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2400594
  CLC： TP391
- Received：14 July 2024，
  
  Revised：11 October 2024，
  
  Published：2025-05
- Accepted：
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Li CHEN, Fan ZHANG, Guangwei XIE, et al. S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion[J]. Frontiers of information technology & electronic engineering, 2025, 26(5): 713-727.
DOI：

Li CHEN, Fan ZHANG, Guangwei XIE, et al. S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion[J]. Frontiers of information technology & electronic engineering, 2025, 26(5): 713-727. DOI： 10.1631/FITEE.2400594.

摘要

人工神经网络（ANN）在遥感影像目标检测方面取得显著进展。然而，低检测效率和高能耗一直是遥感领域的重要瓶颈。脉冲神经网络（SNN）以稀疏脉冲的形式处理信息，为计算机视觉任务带来高效能优势。不过，大部分研究工作集中在简单分类任务上，仅有少数研究者将其应用于自然图像的目标检测。本文考虑到生物大脑的简约特性，提出一种人工—脉冲神经网络快速转换方法，用于遥感影像检测。基于群组稀疏特征建立快速稀疏模型进行脉冲序列感知，并对原始图像进行变换域内的稀疏重采样，从而快速感知图像特征和编码的脉冲序列。此外，为满足相关遥感场景中的精度要求，从理论上分析了转换误差，提出通道自衰减加权归一化方法，以消除神经元过度激活。所提遥感影像目标检测模型被称作S3Det。基于一个大型公开遥感数据集的实验表明，S3Det实现了与ANN相似的精度。同时，我们的转换网络稀疏度为原始算法的24.32%；能耗仅为1.46 W，是原始算法的1/122。

Abstract

Artificial neural networks (ANNs) have made great strides in the field of remote sensing image object detection. However

low detection efficiency and high power consumption have always been significant bottlenecks in remote sensing. Spiking neural networks (SNNs) process information in the form of sparse spikes

creating the advantage of high energy efficiency for computer vision tasks. However

most studies have focused on simple classification tasks

and only a few researchers have applied SNNs to object detection in natural images. In this study

we consider the parsimonious nature of biological brains and propose a fast ANN-to-SNN conversion method for remote sensing image detection. We establish a fast sparse model for pulse sequence perception based on group sparse features and conduct transform-domain sparse resampling of the original images to enable fast perception of image features and encoded pulse sequences. In addition

to meet accuracy requirements in relevant remote sensing scenarios

we theoretically analyze the transformation error and propose channel self-decaying weighted normalization (CSWN) to eliminate neuron overactivation. We propose S3Det

a remote sensing image object detection model. Our experiments

based on a large publicly available remote sensing dataset

show that S3Det achieves an accuracy performance similar to that of the ANN. Meanwhile

our transformed network is only 24.32% as sparse as the benchmark and consumes only 1.46 W

which is 1/122 of the original algorithm’s power consumption.

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