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基于DT-LIF神經(jīng)元與SSD的脈沖神經(jīng)網(wǎng)絡(luò)目標(biāo)檢測方法

周雅 栗心怡 武喜艷 趙宇飛 宋勇

周雅, 栗心怡, 武喜艷, 趙宇飛, 宋勇. 基于DT-LIF神經(jīng)元與SSD的脈沖神經(jīng)網(wǎng)絡(luò)目標(biāo)檢測方法[J]. 電子與信息學(xué)報, 2023, 45(8): 2722-2730. doi: 10.11999/JEIT221367
引用本文: 周雅, 栗心怡, 武喜艷, 趙宇飛, 宋勇. 基于DT-LIF神經(jīng)元與SSD的脈沖神經(jīng)網(wǎng)絡(luò)目標(biāo)檢測方法[J]. 電子與信息學(xué)報, 2023, 45(8): 2722-2730. doi: 10.11999/JEIT221367
ZHOU Ya, LI Xinyi, WU Xiyan, ZHAO Yufei, SONG Yong. Object Detection Method with Spiking Neural Network Based on DT-LIF Neuron and SSD[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2722-2730. doi: 10.11999/JEIT221367
Citation: ZHOU Ya, LI Xinyi, WU Xiyan, ZHAO Yufei, SONG Yong. Object Detection Method with Spiking Neural Network Based on DT-LIF Neuron and SSD[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2722-2730. doi: 10.11999/JEIT221367

基于DT-LIF神經(jīng)元與SSD的脈沖神經(jīng)網(wǎng)絡(luò)目標(biāo)檢測方法

doi: 10.11999/JEIT221367
基金項目: 國家自然科學(xué)基金(82272130, U22A20103)
詳細信息
    作者簡介:

    周雅:女,副教授,研究方向為智能光電信息處理

    栗心怡:女,碩士生,研究方向為類腦計算

    武喜艷:女,博士生,研究方向為脈沖神經(jīng)網(wǎng)絡(luò)及其應(yīng)用

    趙宇飛:男,博士后,研究方向為面向計算機視覺的類腦計算

    宋勇:男,教授,研究方向為類腦計算、智能交互等

    通訊作者:

    宋勇 yongsong@bit.edu.cn

  • 中圖分類號: TN911.73; TP391.41

Object Detection Method with Spiking Neural Network Based on DT-LIF Neuron and SSD

Funds: The National Natural Science Foundation of China (82272130, U22A20103)
  • 摘要: 相對于傳統(tǒng)人工神經(jīng)網(wǎng)絡(luò)(ANN),脈沖神經(jīng)網(wǎng)絡(luò)(SNN)具有生物可解釋性、計算效率高等優(yōu)勢。然而,對于目標(biāo)檢測任務(wù),SNN存在訓(xùn)練難度大、精度低等問題。針對上述問題,該文提出一種基于動態(tài)閾值LIF神經(jīng)元(DT-LIF)與單鏡頭多盒檢測器(SSD)的SNN目標(biāo)檢測方法。首先,設(shè)計了一種DT-LIF神經(jīng)元模型,該模型可根據(jù)累積的膜電位動態(tài)調(diào)整神經(jīng)元的閾值,以驅(qū)動深層網(wǎng)絡(luò)的脈沖活動,提高推理速度。同時,以DT-LIF神經(jīng)元為基元,構(gòu)建了一種基于SSD的混合SNN。該網(wǎng)絡(luò)以脈沖視覺幾何群網(wǎng)絡(luò)(Spiking VGG)和脈沖密集連接卷積網(wǎng)絡(luò)(Spiking DenseNet)為主干(Backbone),具有由批處理歸一化(BN)層、脈沖卷積(SC)層與DT-LIF神經(jīng)元構(gòu)成的3個額外層和SSD預(yù)測框頭(Head)。實驗結(jié)果表明,相對于LIF神經(jīng)元網(wǎng)絡(luò),DT-LIF神經(jīng)元網(wǎng)絡(luò)在Prophesee GEN1數(shù)據(jù)集上的目標(biāo)檢測精度提高了25.2%。對比AsyNet算法,所提方法的目標(biāo)檢測精度提高了17.9%。
  • 圖  1  LIF神經(jīng)元模型等效電路

    圖  2  基于DT-LIF神經(jīng)元與SSD的目標(biāo)檢測算法的結(jié)構(gòu)

    圖  3  DT-LIF神經(jīng)元模型示意圖

    圖  4  Spiking VGG網(wǎng)絡(luò)結(jié)構(gòu)圖(以VGG11為例)

    圖  5  Spiking DenseNet網(wǎng)絡(luò)結(jié)構(gòu)圖(以DenseNet121為例)

    圖  6  Prophesee GEN1數(shù)據(jù)集示例

    圖  7  訓(xùn)練損失(Loss)曲線圖

    算法1 DT-LIF發(fā)射脈沖過程
     參數(shù):θ, p, q, Vth, τm
     (1) θ = Vth = 1; V = 0; Vreset = 0 // 初始化
     (2) for t = 1 to timesteps do
     (3)  for l = 2 to L do
     (4)   for i = 1 to neurons do
     (5)    $ H_{i,t}^l $ = $ V_{i,t-1}^l $ + ($ X_{i,t}^l $ – ($ V_{i,t-1}^l $ – Vreset)) * tau // $ X_{i,t}^l $
          是正向傳遞的輸入
     (6)    delta = $ H_{i,t}^l $ – $ V_{i,t-1}^l $
     (7)    $\theta_{i,t}^l $ = p + q exp (–delta / c)
     (8)    if $ H_{i,t}^l $ ≥ $\theta_{i,t}^l $ then
     (9)     $ S_{i,t}^l $ = 1
     (10)     $ V_{i,t}^l $ = Vreset
     (11)    end for
     (12)   end for
     (13) end for
    下載: 導(dǎo)出CSV

    表  1  Prophesee GEN1數(shù)據(jù)集上的對比實驗結(jié)果

    方法mAP(0.5:0.95)
    Spiking VGG11+LIF0.127
    Spiking VGG11+DT-LIF0.159
    Spiking DenseNet+LIF0.148
    Spiking DenseNet+DT-LIF0.165
    AsyNet[31]0.140
    下載: 導(dǎo)出CSV
  • [1] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.
    [2] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904–1916. doi: 10.1109/TPAMI.2015.2389824
    [3] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779–788.
    [4] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37.
    [5] TAN Mingxing and LE Q. EfficientNet: Rethinking model scaling for convolutional neural networks[C]. The 36th International Conference on Machine Learning, Long Beach, USA, 2019: 6105–6114.
    [6] GERSTNER W and KISTLER W M. Spiking Neuron Models: Single Neurons, Populations, Plasticity[M]. Cambridge: Cambridge University Press, 2002: 421–454.
    [7] KIM S, PARK S, NA B, et al. Spiking-YOLO: Spiking neural network for energy-efficient object detection[C]. The 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020: 11270–11277.
    [8] CHAKRABORTY B, SHE Xueyuan, and MUKHOPADHYAY S. A fully spiking hybrid neural network for energy-efficient object detection[J]. IEEE Transactions on Image Processing, 2021, 30: 9014–9029. doi: 10.1109/TIP.2021.3122092
    [9] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2999–3007.
    [10] KUGELE A, PFEIL T, PFEIFFER M, et al. Hybrid SNN-ANN: Energy-efficient classification and object detection for event-based vision[C]. 43rd DAGM German Conference on Pattern Recognition, Bonn, Germany, 2022: 297–312.
    [11] 胡一凡, 李國齊, 吳郁杰, 等. 脈沖神經(jīng)網(wǎng)絡(luò)研究進展綜述[J]. 控制與決策, 2021, 36(1): 1–26. doi: 10.13195/j.kzyjc.2020.1006

    HU Yifan, LI Guoqi, WU Yujie, et al. Spiking neural networks: A survey on recent advances and new directions[J]. Control and Decision, 2021, 36(1): 1–26. doi: 10.13195/j.kzyjc.2020.1006
    [12] TOYOIZUMI T, PFISTER J P, AIHARA K, et al. Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model[C]. The 17th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2004: 1409–1416.
    [13] HEBB D O. The Organization of Behavior: A Neuropsychological Theory[M]. New York: Psychology Press, 2002.
    [14] KHERADPISHEH S R, GANJTABESH M, THORPE S J, et al. STDP-based spiking deep convolutional neural networks for object recognition[J]. Neural Networks, 2018, 99: 56–67. doi: 10.1016/j.neunet.2017.12.005
    [15] DIEHL P U, NEIL D, BINAS J, et al. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing[C]. 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 2015: 1–8.
    [16] NEFTCI E O, MOSTAFA H, and ZENKE F. Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks[J]. IEEE Signal Processing Magazine, 2019, 36(6): 51–63. doi: 10.1109/msp.2019.2931595
    [17] WU Yujie, DENG Lei, LI Guoqi, et al. Spatio-temporal backpropagation for training high-performance spiking neural networks[J]. Frontiers in Neuroscience, 2018, 12: 331. doi: 10.3389/fnins.2018.00331
    [18] ZHENG Hanle, WU Yujie, DENG Lei, et al. Going deeper with directly-trained larger spiking neural networks[C]. The 35th AAAI Conference on Artificial Intelligence, Palo Alto, USA, 2021: 11062–11070.
    [19] FANG Wei, YU Zhaofei, CHEN Yanqi, et al. Incorporating learnable membrane time constant to enhance learning of spiking neural networks[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 2641–2651.
    [20] GERSTNER W, KISTLER W M, NAUD R, et al. Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition[M]. Cambridge: Cambridge University Press, 2014.
    [21] 賀豐收, 何友, 劉準(zhǔn)釓, 等. 卷積神經(jīng)網(wǎng)絡(luò)在雷達自動目標(biāo)識別中的研究進展[J]. 電子與信息學(xué)報, 2020, 42(1): 119–131. doi: 10.11999/JEIT180899

    HE Fengshou, HE You, LIU Zhunga, et al. Research and development on applications of convolutional neural networks of radar automatic target recognition[J]. Journal of Electronics &Information Technology, 2020, 42(1): 119–131. doi: 10.11999/JEIT180899
    [22] 董小偉, 韓悅, 張正, 等. 基于多尺度加權(quán)特征融合網(wǎng)絡(luò)的地鐵行人目標(biāo)檢測算法[J]. 電子與信息學(xué)報, 2021, 43(7): 2113–2120. doi: 10.11999/JEIT200450

    DONG Xiaowei, HAN Yue, ZHANG Zheng, et al. Metro pedestrian detection algorithm based on multi-scale weighted feature fusion network[J]. Journal of Electronics &Information Technology, 2021, 43(7): 2113–2120. doi: 10.11999/JEIT200450
    [23] SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]. 3rd International Conference on Learning Representations, San Diego, USA, 2015.
    [24] AZOUZ R and GRAY C M. Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo[J]. Proceedings of the National Academy of Sciences of the United States of America, 2000, 97(14): 8110–8115. doi: 10.1073/PNAS.130200797
    [25] FONTAINE B, PE?A J L, and BRETTE R. Spike-threshold adaptation predicted by membrane potential dynamics in vivo[J]. PLoS Computational Biology, 2014, 10(4): e1003560. doi: 10.1371/journal.PCBI.1003560
    [26] XIAO Rong, TANG Huajin, MA Yuhao, et al. An event-driven categorization model for AER image sensors using multispike encoding and learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(9): 3649–3657. doi: 10.1109/tnnls.2019.2945630
    [27] FANG Wei, YU Zhaofei, CHEN Yanqi, et al. Deep residual learning in spiking neural networks[C/OL]. The 34th International Conference on Neural Information Processing Systems, 2021: 21056–21069.
    [28] HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2261–2269.
    [29] DE TOURNEMIRE P, NITTI D, PEROT E, et al. A large scale event-based detection dataset for automotive[EB/OL]. https://doi.org/10.48550/arXiv.2001.08499, 2020.
    [30] 張德祥, 王俊, 袁培成. 基于注意力機制的多尺度全場景監(jiān)控目標(biāo)檢測方法[J]. 電子與信息學(xué)報, 2022, 44(9): 3249–3257. doi: 10.11999/JEIT210664

    ZHANG Dexiang, WANG Jun, and YUAN Peicheng. Object detection method for multi-scale full-scene surveillance based on attention mechanism[J]. Journal of Electronics &Information Technology, 2022, 44(9): 3249–3257. doi: 10.11999/JEIT210664
    [31] MESSIKOMMER N, GEHRIG D, LOQUERCIO A, et al. Event-based asynchronous sparse convolutional networks[C]. 16th European Conference on Computer Vision, Glasgow, UK, 2020: 415–431.
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  • 收稿日期:  2022-11-01
  • 修回日期:  2023-05-11
  • 網(wǎng)絡(luò)出版日期:  2023-05-20
  • 刊出日期:  2023-08-21

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