基于对偶误差的脉冲神经网络目标检测方法

刘伟; 李文娟; 高晋; 李椋

doi:10.11999/JEIT221549

基于对偶误差的脉冲神经网络目标检测方法

doi: 10.11999/JEIT221549

刘伟^1, ,,
李文娟¹,
高晋¹,
李椋²

1.
中国科学院自动化研究所模式识别国家重点实验室北京 100098
2.
军事认知与脑科学研究所北京 100850

基金项目: 国家重点研发计划(2020AAA0105802, 2020AAA0105800)，国家自然科学基金(62202469)，北京市自然科学基金(4224091)

详细信息

作者简介:
刘伟：男，助理研究员，研究方向为计算机视觉及其应用

李文娟：女，副研究员，研究方向为图像处理与模式识别

高晋：男，副研究员，研究方向为视觉智能感知及其在无人系统中的应用

李椋：男，助理研究员，研究方向为类脑视觉感知

通讯作者:
刘伟　liuwei@ia.ac.cn

中图分类号: TP183
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- 被引次数: 0
出版历程
- 收稿日期: 2022-12-15
- 修回日期: 2023-05-25
- 网络出版日期: 2023-06-09
- 刊出日期: 2023-12-26

Spiking Neural Network for Object Detection Based on Dual Error

LIU Wei^{1
, ,},
LI Wenjuan¹,
GAO Jin¹,
LI Liang²

1.
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100098, China
2.
Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, China

Funds: The National Key Research and Development Program of China (2020AAA0105802, 2020AAA0105800), The National Natural Science Foundation of China (62202469), Beijing Natural Science Foundation (4224091)

摘要

摘要: 脉冲神经网络(SNN)是一种模拟大脑神经元动力学的低功耗神经网络，为高计算效率、低能源消耗环境部署目标检测任务提供了可行的解决方案。由于脉冲的不可微性质导致SNN训练困难，一种有效的解决方法是将预训练的人工神经网络(ANN)转换为SNN来提高推理能力。然而，转换后的SNN 经常遇到性能下降和高延迟的问题，无法满足目标检测任务对高精度定位的要求。该文针对ANN转SNN过程中产生的误差问题，引入对偶误差模型降低转换性能损失。首先，该文分析误差产生原因，构建对偶误差模型来模拟ANN到SNN转换误差。进一步地，将对偶误差模型引入到ANN训练过程，使转换前后的模型在训练和推理过程中误差保持一致，从而降低模型的转换性能损失。最后，利用轻量化检测算法YOLO在数据集PASCAL VOC和 MS COCO上验证了对偶误差模型的有效性。
- 脉冲神经网络 /
- 目标检测 /
- 转换误差 /
- 对偶误差模型
Abstract: A Spiking Neural Network (SNN) is a low-power neural network that simulates the dynamics of neurons in the brain, providing a feasible solution for deploying object detection tasks in high computational efficiency and low energy consumption environments. Due to the non-differentiable nature of spikes, SNN training is difficult, and a practical solution is to convert pre-trained Artificial Neural Networks (ANNs) into SNNs to improve inference ability. However, the converted SNN often suffers from performance degradation and high latency, which can not meet the high-precision localization requirements for object detection tasks. A dual error is introduced to reduce the loss of conversion performance. To simulate the ANN to SNN conversion error, the causes of errors are analyzed, and a dual error model is built. Further, the dual error model is introduced into the ANN training process so that the errors of the models before and after conversion remain consistent during training and testing, thereby reducing the loss of conversion performance. Finally, the lightweight detection algorithm YOLO is used to verify the effectiveness of the dual error model on the PASCAL VOC and MS COCO datasets.
- Spiking Neural Network (SNN) /
- Object detection /
- Conversion error /
- Dual error model

HTML全文

图 1 网络结构模型

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图 2 对偶误差模型

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图 3 目标检测结果

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图 4 对偶误差模型推理结果

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表 1 MS COCO和PASCAL VOC数据集上mAP实验结果(%)

模型 ANN SNN
COCO VOC COCO VOC

Tiny YOLO 26.24 53.01 – –
Spiking YOLO(T =5000) 26.24 53.01 25.66 51.83
本文方法(T =300) 38.70 54.50 30.81 51.70

下载: 导出CSV

表 2 不同时间步长在两个数据集上mAP的结果(%)

T COCO VOC

300 30.81 51.70
200 25.60 49.30
100 0.13 31.20
50 0.00 0.06

下载: 导出CSV

表 3 对偶误差消融实验mAP(%)

模型 COCO VOC

w\o DE-SNN(T=300) 28.86 50.23
w\o DE-SNN(T=200) 23.16 48.63
DE-SNN(T=300) 30.81 51.70
DE-SNN(T=200) 25.60 49.30

下载: 导出CSV

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