Spiking Neural Network for Ultralow-Latency and High-Accurate Object Detection
Jinye Qu1; Zeyu Gao1; Tielin Zhang1; Yanfeng Lu1; Huajin Tang2; Hong Qiao1
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
2024
Pages10.1109/TNNLS.2024.3372613
Corresponding AuthorLu, Yanfeng()
Abstract

Spiking Neural Networks (SNNs) have attracted
significant attention for their energy-efficient and brain-inspired
event-driven properties. Recent advancements, notably Spiking-
YOLO, have enabled SNNs to undertake advanced object
detection tasks. Nevertheless, these methods often suffer from
increased latency and diminished detection accuracy, rendering
them less suitable for latency-sensitive mobile platforms. Additionally,
the conversion of artificial neural networks (ANNs)
to SNNs frequently compromises the integrity of the ANNs’
structure, resulting in poor feature representation and heightened
conversion errors. To address the issues of high latency and
low detection accuracy, we introduce two solutions: timestep
compression and spike-time-dependent integrated (STDI) coding.
Timestep compression effectively reduces the number of timesteps
required in the ANN-to-SNN conversion by condensing information.
The STDI coding employs a time-varying threshold to
augment information capacity. Furthermore, we have developed
an SNN-based spatial pyramid pooling (SPP) structure, optimized
to preserve the network’s structural efficacy during conversion.
Utilizing these approaches, we present the ultralow latency and
highly accurate object detection model, SUHD. SUHD exhibits
exceptional performance on challenging datasets like PASCAL
VOC and MS COCO, achieving a remarkable reduction of
approximately 750 times in timesteps and a 30% enhancement in
mean average precision (mAP) compared to Spiking-YOLO on
MS COCO. To the best of our knowledge, SUHD is currently the
deepest spike-based object detection model, achieving ultralow
timesteps for lossless conversion.

KeywordLow latency object detection spiking neural network (SNN) timesteps compression
DOI10.1109/TNNLS.2024.3372613
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Plan of China
Funding OrganizationNational Key Research and Development Plan of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:001189568000001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification类脑模型与计算
planning direction of the national heavy laboratory认知机理与类脑学习
Paper associated data
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57282
Collection多模态人工智能系统全国重点实验室_机器人理论与应用
Corresponding AuthorYanfeng Lu
Affiliation1.中国科学院自动化研究所
2.浙江大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Jinye Qu,Zeyu Gao,Tielin Zhang,et al. Spiking Neural Network for Ultralow-Latency and High-Accurate Object Detection[J]. IEEE Transactions on Neural Networks and Learning Systems,2024:10.1109/TNNLS.2024.3372613.
APA Jinye Qu,Zeyu Gao,Tielin Zhang,Yanfeng Lu,Huajin Tang,&Hong Qiao.(2024).Spiking Neural Network for Ultralow-Latency and High-Accurate Object Detection.IEEE Transactions on Neural Networks and Learning Systems,10.1109/TNNLS.2024.3372613.
MLA Jinye Qu,et al."Spiking Neural Network for Ultralow-Latency and High-Accurate Object Detection".IEEE Transactions on Neural Networks and Learning Systems (2024):10.1109/TNNLS.2024.3372613.
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