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Suppl. Mater.: 
CLC number: TP391
On-line Access: 2025-06-04
Received: 2024-07-14
Revision Accepted: 2024-10-11
Crosschecked: 2025-06-04
Cited: 0
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S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion
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Li CHEN, Fan ZHANG, Guangwei XIE, Yanzhao GAO, Xiaofeng QI, Mingqian SUN. 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.
@article{title="S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion",
author="Li CHEN, Fan ZHANG, Guangwei XIE, Yanzhao GAO, Xiaofeng QI, Mingqian SUN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="26",
number="5",
pages="713-727",
year="2025",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400594"
}
%0 Journal Article
%T S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion
%A Li CHEN
%A Fan ZHANG
%A Guangwei XIE
%A Yanzhao GAO
%A Xiaofeng QI
%A Mingqian SUN
%J Frontiers of Information Technology & Electronic Engineering
%V 26
%N 5
%P 713-727
%@ 2095-9184
%D 2025
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400594
TY - JOUR
T1 - S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion
A1 - Li CHEN
A1 - Fan ZHANG
A1 - Guangwei XIE
A1 - Yanzhao GAO
A1 - Xiaofeng QI
A1 - Mingqian SUN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 26
IS - 5
SP - 713
EP - 727
%@ 2095-9184
Y1 - 2025
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400594
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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