一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級(jí)搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機(jī)號(hào)碼
標(biāo)題
留言內(nèi)容
驗(yàn)證碼

基于非局部操作的深度卷積神經(jīng)網(wǎng)絡(luò)車位占用檢測算法

申鉉京 沈哲 黃永平 王玉

申鉉京, 沈哲, 黃永平, 王玉. 基于非局部操作的深度卷積神經(jīng)網(wǎng)絡(luò)車位占用檢測算法[J]. 電子與信息學(xué)報(bào), 2020, 42(9): 2269-2276. doi: 10.11999/JEIT190349
引用本文: 申鉉京, 沈哲, 黃永平, 王玉. 基于非局部操作的深度卷積神經(jīng)網(wǎng)絡(luò)車位占用檢測算法[J]. 電子與信息學(xué)報(bào), 2020, 42(9): 2269-2276. doi: 10.11999/JEIT190349
Xuanjing SHEN, Zhe SHEN, Yongping HUANG, Yu WANG. Deep Convolutional Neural Network for Parking Space Occupancy Detection Based on Non-local Operation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2269-2276. doi: 10.11999/JEIT190349
Citation: Xuanjing SHEN, Zhe SHEN, Yongping HUANG, Yu WANG. Deep Convolutional Neural Network for Parking Space Occupancy Detection Based on Non-local Operation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2269-2276. doi: 10.11999/JEIT190349

基于非局部操作的深度卷積神經(jīng)網(wǎng)絡(luò)車位占用檢測算法

doi: 10.11999/JEIT190349
基金項(xiàng)目: 智慧法院智能化服務(wù)技術(shù)研究及支撐平臺(tái)開發(fā)(2018YFC0830100),國家自然科學(xué)基金(61672259, 61876070),國家自然科學(xué)基金青年科學(xué)基金(61602203),吉林省科技發(fā)展計(jì)劃重點(diǎn)科技研發(fā)項(xiàng)目(20180201064SF),吉林省優(yōu)秀青年人才基金(20180520020JH)
詳細(xì)信息
    作者簡介:

    申鉉京:男,1958年生,博士,教授,研究方向?yàn)閳D像處理與模式識(shí)別、多媒體信息安全、智能控制技術(shù)

    沈哲:男,1995年生,碩士生,研究方向?yàn)閳D像處理與模式識(shí)別

    黃永平:男,1964年生,博士,副教授,研究方向?yàn)閳D像處理與模式識(shí)別、智能控制與嵌入式系統(tǒng)

    王玉:男,1983年生,博士,副教授,研究方向?yàn)閳D像處理與模式識(shí)別、多媒體信息技術(shù)

    通訊作者:

    王玉 wangyu001@jlu.edu.cn

  • 中圖分類號(hào): TN911.73; TP391

Deep Convolutional Neural Network for Parking Space Occupancy Detection Based on Non-local Operation

Funds: The Intelligent Court Intelligent Service Technology Research and Support Platform Development (2018YFC0830100), The National Natural Science Foundation of China (61672259, 61876070), The National Natural Science Foundation of China Youth Science Foundation (61602203), The Key Scientific and Technological R & D Projects of Jilin Province Science and Technology Development Plan(20180201064SF), Jilin Province Outstanding Young Talent Fund Project (20180520020JH)
  • 摘要: 隨著城市交通智能化發(fā)展,準(zhǔn)確高效地獲取可用車位對(duì)于解決日益嚴(yán)峻的停車難問題至關(guān)重要。該文提出一種基于非局部操作的深度卷積神經(jīng)網(wǎng)絡(luò)車位占用檢測算法。針對(duì)停車位圖像特性,引入非局部操作,度量遠(yuǎn)距離像素間的相似性,直接獲取邊緣高頻特征;使用小卷積核獲取局部細(xì)節(jié)特征;以端到端的方式訓(xùn)練網(wǎng)絡(luò)。實(shí)驗(yàn)中,通過設(shè)置不同卷積核尺寸和非局部模塊層數(shù),優(yōu)化網(wǎng)絡(luò)結(jié)構(gòu)。實(shí)驗(yàn)結(jié)果表明,該文所提算法與傳統(tǒng)的基于紋理特征的車位占用檢測算法相比,無論在預(yù)測精度還是模型的泛化性能,均具有顯著的優(yōu)勢。與當(dāng)前廣泛應(yīng)用的基于局部特征提取的卷積神經(jīng)網(wǎng)絡(luò)相比,該算法具有較大的優(yōu)勢。在真實(shí)場景中,該算法同樣具有較高精度,具備實(shí)際應(yīng)用價(jià)值。
  • 圖  1  非局部模塊

    圖  2  停車位圖像

    圖  3  模型結(jié)構(gòu)圖

    圖  4  不同卷積核尺寸的準(zhǔn)確率曲線圖

    圖  5  不同層數(shù)非局部模塊的準(zhǔn)確率曲線圖

    圖  6  可視化的特征圖

    圖  7  PKLot, CNRPark數(shù)據(jù)集間實(shí)驗(yàn)準(zhǔn)確率對(duì)比柱狀圖

    圖  8  匡亞明樓停車場車位占用情況檢測結(jié)果

    表  1  不同卷積核尺寸的準(zhǔn)確率詳細(xì)對(duì)比(%)

    卷積核尺寸訓(xùn)練精度測試精度
    UFPR04UFPR05PUCPR
    399.9799.7496.4097.48
    599.9099.7897.6797.85
    799.5699.7296.0096.78
    999.4499.4194.8196.38
    1199.4199.2592.1895.39
    下載: 導(dǎo)出CSV

    表  2  不同層數(shù)非局部模塊的準(zhǔn)確率詳細(xì)對(duì)比(%)

    非局部模塊層數(shù)訓(xùn)練精度測試精度
    UFPR04UFPR05PUCPR
    199.9099.7897.6797.85
    299.9699.8197.6597.55
    399.9599.8598.5598.35
    下載: 導(dǎo)出CSV

    表  3  不同方法的PKLot子數(shù)據(jù)集內(nèi)測試準(zhǔn)確率(%)

    訓(xùn)練集UFPR04UFPR05PUCPR
    測試集UFPR04UFPR05PUCPR
    本文方法99.8599.6299.92
    mAlexnet99.5499.4999.90
    LPQu99.5098.9099.58
    Mean99.6499.3099.61
    下載: 導(dǎo)出CSV

    表  4  不同方法的PKLot子數(shù)據(jù)集間測試準(zhǔn)確率(%)

    訓(xùn)練集測試集方法精度
    UFPR04UFPR05本文方法98.55
    mAlexnet[14]93.29
    LPQg[18]84.92
    Max88.33
    PUCPR本文方法98.31
    mAlexnet[14]98.27
    LPQg[18]84.25
    Mean88.40
    UFPR05UFPR04本文方法94.45
    mAlexnet[14]93.69
    LPQg[18]85.76
    Mean85.53
    PUCPR本文方法95.87
    mAlexnet[14]92.72
    LPQu[17]87.74
    Mean89.83
    PUCPRUFPR04本文方法99.24
    mAlexnet[14]98.03
    LPQg[18]87.15
    Mean88.88
    UFPR05本文方法98.89
    mAlexnet[14]96.00
    LBPri[19]82.78
    Mean84.20
    下載: 導(dǎo)出CSV
  • CAICEDO F, BLAZQUEZ C, and MIRANDA P. Prediction of parking space availability in real time[J]. Expert Systems with Applications, 2012, 39(8): 7281–7290. doi: 10.1016/j.eswa.2012.01.091
    DEL POSTIGO C G, TORRES J, and MENéNDEZ J M. Vacant parking area estimation through background subtraction and transience map analysis[J]. IET Intelligent Transport Systems, 2015, 9(9): 835–841. doi: 10.1049/iet-its.2014.0090
    DAN N. Parking management system and method[P]. US, 20030144890, 2003.
    TSAI L W, HSIEH J W, and FAN K C. Vehicle detection using normalized color and edge map[J]. IEEE Transactions on Image Processing, 2007, 16(3): 850–864. doi: 10.1109/tip.2007.891147
    HUANG C C, TAI Yushu, and WANG S J. Vacant parking space detection based on plane-based Bayesian hierarchical framework[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(9): 1598–1610. doi: 10.1109/tcsvt.2013.2254961
    DELIBALTOV D, WU Wencheng, LOCE R P, et al. Parking lot occupancy determination from lamp-post camera images[C]. The 16th International IEEE Conference on Intelligent Transportation Systems, The Hague, Netherlands, 2013: 2387–2392. doi: 10.1109/itsc.2013.6728584.
    LECUN Y, BENGIO Y, and HINTON G E. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539
    DE ALMEID P R L, OLIVEIRA L S, BRITTO JR A S, et al. PKLot–a robust dataset for parking lot classification[J]. Expert Systems with Applications, 2015, 42(11): 4937–4949. doi: 10.1016/j.eswa.2015.02.009
    AMATO G, CARRARA F, FALCHI F, et al. Car parking occupancy detection using smart camera networks and deep learning[C]. 2016 IEEE Symposium on Computers and Communication, Messina, Italy, 2016: 1212–1217. doi: 10.1109/iscc.2016.7543901.
    BUADES A, COLL B, and MOREL J M. A non-local algorithm for image denoising[C]. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005: 60–65. doi: 10.1109/cvpr.2005.38.
    VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]. The 31st Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 5998–6008.
    WANG Xiaolong, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7794–7803. doi: 10.1109/cvpr.2018.00813.
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778. doi: 10.1109/cvpr.2016.90.
    AMATO G, CARRARA F, FALCHI F, et al. Deep learning for decentralized parking lot occupancy detection[J]. Expert Systems with Applications, 2017, 72: 327–334. doi: 10.1016/j.eswa.2016.10.055
    KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[C]. The 25th International Conference on Neural Information Processing Systems, Red Hook, USA, 2012: 1097–1105.
    NURULLAYEV S and LEE S W. Generalized parking occupancy analysis based on dilated convolutional neural network[J]. Sensors, 2019, 19(2): 277. doi: 10.3390/s19020277
    OJANSIVU V and HEIKKIL? J. Blur insensitive texture classification using local phase quantization[C]. The 3rd International Conference on Image and Signal Processing, Cherbourg-Octeville, France, 2008: 236–243. doi: 10.1007/978-3-540-69905-7_27.
    RAHTU E, HEIKKILA J, OJANSIVU V, et al. Local phase quantization for blur-insensitive image analysis[J]. Image and Vision Computing, 2012, 30(8): 501–512. doi: 10.1016/j.imavis.2012.04.001
    OJALA T, PIETIKAINEN M, and MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971–987. doi: 10.1109/tpami.2002.1017623
  • 加載中
圖(8) / 表(4)
計(jì)量
  • 文章訪問數(shù):  2020
  • HTML全文瀏覽量:  805
  • PDF下載量:  101
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2019-05-17
  • 修回日期:  2020-01-04
  • 網(wǎng)絡(luò)出版日期:  2020-07-01
  • 刊出日期:  2020-09-27

目錄

    /

    返回文章
    返回