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基于异常声音的隧道交通事故检测方法

马庆禄 付冰琳 马恋 李杨梅

马庆禄, 付冰琳, 马恋, 李杨梅. 基于异常声音的隧道交通事故检测方法[J]. 交通信息与安全, 2023, 41(1): 34-42. doi: 10.3963/j.jssn.1674-4861.2023.01.004
引用本文: 马庆禄, 付冰琳, 马恋, 李杨梅. 基于异常声音的隧道交通事故检测方法[J]. 交通信息与安全, 2023, 41(1): 34-42. doi: 10.3963/j.jssn.1674-4861.2023.01.004
MA Qinglu, FU Binglin, MA Lian, LI Yangmei. A Method for Detecting Traffic Accidents on Highway Tunnel Sections Based on Abnormal Sound[J]. Journal of Transport Information and Safety, 2023, 41(1): 34-42. doi: 10.3963/j.jssn.1674-4861.2023.01.004
Citation: MA Qinglu, FU Binglin, MA Lian, LI Yangmei. A Method for Detecting Traffic Accidents on Highway Tunnel Sections Based on Abnormal Sound[J]. Journal of Transport Information and Safety, 2023, 41(1): 34-42. doi: 10.3963/j.jssn.1674-4861.2023.01.004

基于异常声音的隧道交通事故检测方法

doi: 10.3963/j.jssn.1674-4861.2023.01.004
基金项目: 

国家重点研发计划项目 2018YFB1600200

重庆市研究生科研创新项目 CYS21356

详细信息
    通讯作者:

    马庆禄(1980—),博士,教授. 研究方向:智能交通与安全. E-mail:mql360@qq.com

  • 中图分类号: U412.6

A Method for Detecting Traffic Accidents on Highway Tunnel Sections Based on Abnormal Sound

  • 摘要: 针对公路隧道内交通事故的动态感知问题,在传统检测方法的基础上引入声学检测理论与方法,研究基于异常声音检测的隧道交通事故智能检测方法。通过分析短时能量(short term energy,STE)和梅尔倒谱系数(Mel-scale frequency cepstral coefficients,MFCC)检测方法在事故段特征表征以及精度干扰方面的缺陷,提出1种改进的融合特征MFCCE研究隧道环境下的交通事故检测。提取STE和MFCC特征并使用主成分分析(principal component analysis,PCA)进行特征融合得到新的融合特征MFCCE。以真实行车事故数据为基础,构建包含刹车与碰撞声的2段隧道噪声实验样本数据,分别对应早高峰时段(07:00—08:00)及平峰时段(12:00—13:00)的行车条件对隧道内的事故环境进行模拟分析,利用端点检测对所提方法进行验证并与其余2种方法进行对比分析。使用Pearson简单相关系数法作为最终的评价方法,通过该方法计算得到的相关系数r对比三种检测结果与原始样本的正相关相性。实验结果表明:STE在平峰及早高峰时段的相关系数分别为0.933和0.988;MFCC在平峰及早高峰时段的相关系数均为0.998;而无论在平峰还是早高峰时段,MFCCE的相关系数(0.999)均高于另外其他2种检测方法。MFCCE的平均相关系数相比于其他2种检测方法(STE、MFCC)分别提高了3.95%和1.00%。

     

  • 图  1  改进谱减法流程对比

    Figure  1.  Comparison of improved spectral subtraction process

    图  2  实际数据构建

    Figure  2.  Actual data construction

    图  3  隧道环境噪声的采集

    Figure  3.  Collecting tunnel ambient noise

    图  4  4个时段采样数据的原始波形示意图

    Figure  4.  The original waveform diagram of the sampled data in four periods

    图  5  实际数据构建

    Figure  5.  Actual data construction

    图  6  样本数据降噪前后对比图

    Figure  6.  Comparison of sample data before and after noise reduction

    图  7  平峰及高峰时段3种检测方法结果

    Figure  7.  The results of three detection methods in flat peak period and peak period

    图  8  样本检测方法结果对比图

    Figure  8.  Sample detection method results comparison diagram

    表  1  实际数据与合成样本的参数对比

    Table  1.   Comparison of actual data with synthetic samples

    对比参数 实际数据 合成数据 匹配度/%
    声时历程/s 10.3 10.2 99.03
    频率/Hz 48 000 48 000 100
    响度/sone 31.62 29.47 93.20
    尖锐度/acum 1.78 1.62 91.01
    粗糙度/asper 0.99 0.90 90.91
    波动度/vacil 0.40 0.36 90.00
    下载: 导出CSV

    表  1  平峰时段检测结果对比

    Table  1.   Comparison of three detection results in flat peak period  单位: s

    算法 tsb teb tsc tec
    REAL 3.50 4.50 5.00 7.50
    STE 3.31 4.71 4.93 7.25
    MFCC 3.46 4.58 4.98 8.19
    MFCCE 3.46 4.58 4.99 7.40
    注:tsbteb为刹车段的开始时刻与结束时刻;tsctec为碰撞段的开始时刻与结束时刻;REAL为原始样本。
    下载: 导出CSV

    表  2  早高峰时段检测结果对比

    Table  2.   Comparison of detection results during morning peak hours  单位: s

    算法 tsb teb tsc tec
    REAL 3.50 4.50 5.00 7.50
    STE 3.61 4.74 5.00 6.58
    MFCC 3.46 4.58 5.00 8.05
    MFCCE 3.63 4.73 5.00 7.41
    下载: 导出CSV

    表  3  3种检测方法Pearson简单相关系数分析对比

    Table  3.   Pearson simple correlation coefficient analysis and comparison of three detection methods

    算法 r1 r2 p1 p2
    STE 0.933 0.988 0.007 0.012
    MFCC 0.998 0.998 0.002 0.002
    MFCCE 0.999 0.999 0.001 0.001
    注:r1p1为平峰时段的相关系数及概率p值;r2p2为高峰时段的相关系数及概率p值。
    下载: 导出CSV
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  • 收稿日期:  2022-03-07
  • 网络出版日期:  2023-05-13

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