基于多层脉冲神经网络的非接触液位检测方法

张季伦; 朱毅; 李颖; 陈方; 刘颖; 屈鸿

doi:10.11999/JEIT221388

基于多层脉冲神经网络的非接触液位检测方法

doi: 10.11999/JEIT221388

张季伦¹,
朱毅²,
李颖²,
陈方²,
刘颖^1, ,,
屈鸿¹

1.
电子科技大学计算机科学与工程学院成都 611731
2.
北京小米移动软件有限公司北京 100085

基金项目: 国家重点研发计划(2018AAA0100202)，四川省科技计划(2022YFG0313)

详细信息

作者简介:
张季伦：男，博士生，研究方向为类脑计算

朱毅：男，硕士，研究方向为水过滤处理和蒸/烤技术等

李颖：男，硕士，研究方向为厨房家电

陈方：男，博士，研究方向为深度成像、相机技术、集成光学等

刘颖：女，博士生，研究方向为类脑计算

屈鸿：男，教授，研究方向为类脑计算

通讯作者:
刘颖　liuying770315@std.uestc.edu.cn

中图分类号: TN911.73
计量
- 文章访问数: 324
- HTML全文浏览量: 342
- PDF下载量: 87
- 被引次数: 0
出版历程
- 收稿日期: 2022-11-07
- 修回日期: 2023-06-15
- 网络出版日期: 2023-06-22
- 刊出日期: 2023-08-21

Non-contact Liquid Level Detection Method Based on Multilayer Spiking Neural Network

ZHANG Jilun¹,
ZHU Yi²,
LI Ying²,
CHEN Fang²,
LIU Ying^{1
, ,},
QU Hong¹

1.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2.
Beijing Xiaomi Mobile Software Co., Ltd, Beijing 100085, China

Funds: The National Key R&D Program of China (2018AAA0100202), Sichuan Science and Technology Program (2022YFG0313)

摘要

摘要: 尽管基于深度学习的非接触液位检测方法能够较好地完成检测任务，但其对计算资源的较高要求使其不适用于算力受限的嵌入式设备。为解决上述问题，该文首先提出了基于多层脉冲神经网络的非接触液位检测方法；其次，提出了单帧和帧差脉冲编码方法将视频流时间动态性编码成可重构的脉冲模式；最后在实际场景中对模型进行测试。实验结果表明，所提方法具有较高应用价值。
- 脉冲神经网络 /
- 模式识别 /
- LIF神经元 /
- 脉冲编码 /
- 非接触液位测量
Abstract: Although the non-contact liquid level detection method based on deep learning can perform well, its high demand on computational resources makes it not suitable for embedded devices with limited resource. To solve this problem, a non-contact liquid level detection method is first proposed based on multilayer spiking neural network; Furthermore, spiking encoding methods based on single frame and frame difference are proposed to encode the temporal dynamics of video stream into reconfigurable spike patterns; Finally, the model is tested in the real scene. The experimental results show that the proposed method has high application value.
- Spiking Neural Networks (SNNs) /
- Pattern recognition /
- LIF neuron /
- Spiking encoding /
- Non-contact liquid level measurement

HTML全文

图 1 非液接触液面检测示意图

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图 2 基于多层脉冲的非接触液位检测框架

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图 3 脉冲编码

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图 4 神经元抑制过程

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图 5 特征融合层

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图 6 采集装置

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表 1 各类型样本数量

水杯类型	样本数量
玻璃杯	231
保温杯	30
不锈钢杯	93
马克杯	102
瓷杯	180
不透明塑料杯	102
纸杯	63

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表 2 参数$ k $在注水量>70%时平均停水率

感受野尺寸k	纸杯(%)	瓷杯(%)
3	80	83
4	87	85
5	92	92
6	90	89
7	85	87
8	87	80

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表 3 参数$ S $在注水量>70%时平均停水率

脉冲计算阈值	纸杯(%)	瓷杯(%)
130	79	77
135	85	84
140	90	89
145	92	92
150	92	87
155	84	81
160	85	86

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表 4 模型参数取值

层	参数名称	参数值
容器边缘	TC编码阈值	0.5
液面编码层	TC编码阈值	0.5
容器边缘检测层	LIF神经元放电阈值	11
液面检测层	LIF神经元放电阈值	6
容器边缘检测层	侧向抑制范围	1
时空同步层	感受野尺寸k	5
输出与检测层	脉冲计数阈值	145

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表 5 测试结果

序号	操作描述	数据示例	容器边缘编码	液面边缘编码
1	开始注水
2	<20%水量
3	20%～70%水量
4	>70%水量

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表 6 杯子容量—材质阶梯准确率及时停水提示率(%)

容积材质	<20%水量		20%～70%水量		>70%水量
容积材质	P	$ {P_{\text{s}}} $	P	$ {P_{\text{s}}} $	P	$ {P_{\text{s}}} $
马克杯	90	96	92	97	93	97
瓷杯	90	96	92	97	92	97
不透明塑料杯	90	96	93	97	95	97
纸杯	90	96	91	97	92	97
木杯	90	96	91	97	92	97
玻璃杯	–	–	90	97	90	97
不锈钢水温杯	89	96	90	97	92	97
保温杯	–	–	82	97	85	97
锥形玻璃杯	–	–	82	81	83	81
注：表中的“–”表示该类型杯子在市面上不常见，无法采购得到。

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