[논문]음향신호 기반 터널 돌발상황 검지시스템

장진환

doi:10.12815/kits.2019.18.5.112

음향신호 기반 터널 돌발상황 검지시스템
Acoustic Signal-Based Tunnel Incident Detection System 원문보기

韓國ITS學會論文誌 = The journal of the Korea Institute of Intelligent Transportation Systems, v.18 no.5, 2019년, pp.112 - 125

초록
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본 연구에서는 음향신호 처리기반 터널 돌발상황 탐지시스템을 개발하고 평가하였다. 개발 시스템은 알고리즘, 음향신호 수집기, 서버시스템 세 가지 구성 요소로 구성된다. 비음수 텐서 분해와 은닉 마코프 모델을 이용하여 돌발상황음(충돌, 스키드)을 검출한다. 개발시스템 성능은 제한된 환경과 실제 운영환경에서 평가되었다. 그 결과, 제한된 환경 평가에서 거리별로 80~95%의 검지성능을 보였고, 실제 운영환경에서는 94% 검지성능을 보였다. 기존의 터널 돌발상황 검지기술인 영상 및 루프검지기 기반 시스템 성능과 비교한 결과, 본 개발 기술의 장점은 신속한 검지시간(2초 이내)인 것으로 나타났다.

Abstract ▼ AI-Helper

An acoustic signal-based, tunnel-incident detection system was developed and evaluated. The system was comprised of three components: algorithm, acoustic signal collector, and server system. The algorithm, which was based on nonnegative tensor factorization and a hidden Markov model, processes the acoustic signals to attenuate noise and detect incident-related signals. The acoustic signal collector gathers the tunnel sounds, digitalizes them, and transmits the digitalized acoustic signals to the center server. The server system issues an alert once the algorithm identifies an incident. The performance of the system was evaluated thoroughly in two steps: first, in a controlled tunnel environment using the recorded incident sounds, and second, in an uncontrolled tunnel environment using real-world incident sounds. As a result, the detection rates ranged from 80 to 95% at distances from 50 to 10 m in the controlled environment, and 94 % in the uncontrolled environment. The superiority of the developed system to the existing video image and loop detector-based systems lies in its instantaneous detection capability with less than 2 s.

주제어

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제안 방법

(2017), uses the channel gains produced by the NTF method, to detect acoustic signals generated by incident events. An HMM-based likelihood ratio test is then conducted to verify the detected events. The NTF method, which was proposed initially by Shashua and Hazan, has been applied widely to discriminate the event sounds from a range of acoustic sources (FitzGerald et al.
The performance of the developed system was evaluated in two phases: controlled and uncontrolled roadway tunnel environment. For an evaluation in a controlled environment, an unused roadway tunnel and recorded incident sounds were prepared. For the uncontrolled test, six-month long real world incident data from in a tunnel were used.
The developed system is comprised broadly of three elements: acoustic signal processing algorithm, acoustic signal collector, and center server system. An acoustic signal-processing algorithm using nonnegative tensor factorization(NTF) and hidden Markov model (HMM) was developed.
8]. The fiber-optic cable was used for the real-time data transmission, and a LTE-based wireless communication dongle was used for the backup of the incident event data(sound and video image) identified by the algorithm for further analysis.
The performance of the developed system was evaluated in two phases: controlled and uncontrolled roadway tunnel environment. For an evaluation in a controlled environment, an unused roadway tunnel and recorded incident sounds were prepared.
For the uncontrolled test, six-month long real world incident data from in a tunnel were used. Three widely used evaluation indices for incident-detection systems, detection rate (DR), false-alarm rate (FAR), and mean time to detection (MTTD), as expressed in Equations 6 to 8, were used to evaluate the performance.
On the other hand, conventional practices have shown some limitations from the perspective of immediacy, such as traffic detector databased and video image-based algorithms. To resolve the shortcoming, a tunnel-incident detection algorithm using the acoustic signals from crashes and skids was suggested and evaluated thoroughly in real-world situations using abundant data obtained over an eight-month period.

대상 데이터

For an evaluation in a controlled environment, an unused roadway tunnel and recorded incident sounds were prepared. For the uncontrolled test, six-month long real world incident data from in a tunnel were used. Three widely used evaluation indices for incident-detection systems, detection rate (DR), false-alarm rate (FAR), and mean time to detection (MTTD), as expressed in Equations 6 to 8, were used to evaluate the performance.
6]. The acoustic sources for the performance test consisted of real-world 200 crash and 37 skid sounds that were obtained from broadly recognized organizations, including the Euro New Car Assessment Program and the Insurance Institute for Highway Safety of the United States. The sounds were played using a speaker at similar sound pressure levels (SPLs) to those of real sounds.
When initially installed at the site, all parameters of the algorithm were set at the same values, as in the controlled environment. To optimize the algorithm parameters, two-month long acoustic data, including 10 skids and1 crash, were obtained. The collected acoustic signals were classified into two groups: incident and non-incident sounds.

이론/모형

The algorithm identifies incident-prone sounds, among other sounds, produced by moving vehicles. The algorithm, which was initially developed by Jeon et al. (2017), uses the channel gains produced by the NTF method, to detect acoustic signals generated by incident events. An HMM-based likelihood ratio test is then conducted to verify the detected events.

성능/효과

An acoustic signal-processing algorithm using nonnegative tensor factorization(NTF) and hidden Markov model (HMM) was developed. The NTF algorithm was verified to increase the signal-to-noise (SNR) ratio of crash sounds by 27 dB at the maximum, as tested using the tunnel incident sounds. This SNR enhancement is generally considered to be critical for improving the capability of incident detection for any kind of sound-based system.
3] depicts the noise-attenuating procedure by the NTF algorithm. The SNRs for the acoustic signals,which were collected at distances of 10 and 50 m, were improved by 14 and 17 dB on average, according to Equation 5, suggesting that the proposed algorithm can be applied effectively to incident sounds in a tunnel, in which echoes from the wall are the main source of noise.
The real world evaluations, under controlled and uncontrolled environment conditions of the proposed system, resulted in encouraging consequences with DR of 95–80%, FAR of 2.6–3.6%, and MTTD less than 1.4 s for the controlled condition; and DR of 94%, FAR of 7%, and MTTD of 1.8 s for the uncontrolled condition.

참고문헌 (23)

Abdulhai B. and Ritchie S.(1999), "Enhancing the Universality and Transferability of Freeway Incident Detection Using a Bayesian-Based Neural Network," Trans. Res. Part C, vol. 7, no. 5, pp.261-280.

상세보기
Balke K. N.(1993), An Evaluation of Existing Incident Detection Algorithms, Texas Trans. Ins., The Texas A&M Univ. Sys.
Browne R., Foo S., Huynh S., Abdulhai B. and Hall F.(2005), "Comparison and Analysis Tool for Automatic Incident Detection," Trans. Res. Rec., No. 1925.
Castro-Neto M. M., Han L. D., Jeong Y. S. and Jeong M. K.(2012), "Toward Training-Free Automatic Detection of Freeway Incidents: Simple Algorithm with One Parameter," Trans. Res. Rec., No. 2278.
Clavel C., Ehrette T. and Richard, G.(2005), "Events detection for an audio-based surveillance system," Proc. ICME.
European Commission(2001), European Self-Training Workshop, Safety in Tunnels: Experience Feedback and Regulation Prospection.
Fahrtash M.(2012), "Automated Video Incident Detection Systems," Caltrans Div. of Res. and Innov.
FitzGerald D., Cranitch M. and Coyle E.(2005), "Non-Negative Tensor Factorization for Sound Source Separation," ISSC 2005, Dublin.
Foggia P., Saggese A., Strissciuglio N. and Vento M.(2015), "Car crashes detection by audio analysis in crowded roads," Proc. 12th IEEE Int. Con. on Advanced Video and Signal Based Surveillance.
Gemmeke J., Vuegen P. and Vanrumste B.(2013), "An Exemplar-Based NMF Approach to Audio Event Detection," Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New York, USA.
Ghahramani Z.(2001), "An Introduction to Hidden Markov Models and Bayesian Networks," Inter. Jour. of Pattern Recognition and Artificial Intelligence, vol. 15, no. 1, pp.9-41.

상세보기
Hancocks S. and Evans S.(2011), A55 North Wales Tunnels Area Video Automatic Incident Detection, EasyWay, Document number: 5083687/493/03/35695.
Harlow C. and Wang, Y.(2002), "Acoustic Accident Detection System," Jour. of Intelli. Trans. Sys. vol. 7, no. 1, pp.43-56.

상세보기
Jeon K. and Kim H.(2017), "Dual-Channel Acoustic Event Detection in Multisource Environments Using Nonnegative Tensor Factorization and Hidden Markov Model," Jour. of Inst. of Electro. and Infor. Eng., vol. 54, no. 1, pp.121-128.
Kim, H. and Lee, C.(2004), "A Study on the Relationship among Traffic Accidents, Fire Occurrences and Tunnel Characteristics in Local Road Tunnels," Proc. of 2004 Korea Tunnel Eng. Conf., South Korea (in Korean).
Lee H., Kim Y., Kwon T., Park K., Bok K. and Han M.(2004), "An Implementation of Traffic Accident Detection System at Intersection Based on Image and Sound," Jour. of Control, Auto. and Sys. Eng., vol. 10, no. 6, pp.501-509.
Neale W., Terpstra T. and Bortles W.(2008), "Evaluation of Discrete Vehicle Accident Sounds for use in Accident Reconstruction," Proc. Mtgs. Acoust., vol. 5, no. 1.
Ozbay K. and Kachroo P.(1999), Incident Management in Intelligent Transportation Systems, Artech House, ISBN 0-89006-774-0, Boston, London.
Prevedouros P., Ji X., Papandreou K., Kopelias P. and Vegiri, V.(2006), "Video Incident Detection Tests in Freeway Tunnels," Trans. Res. Rec., No. 1959.
Rabaoui A., Davy M., Rossignol S. and Ellouze, N.(2008), "Using one-class SVMs and wavelets for audio surveillance," IEEE Trans. Inf. Forensics Security, vol. 3, no. 4, pp.763-775.

상세보기
Vacher M., Istrate D., Besacier L., Serignat J. F. and Castelli, E.(2004), "Sound Detection and Classification for Medical Telesurvey," 2nd Conf. on Bio. Eng., Austria.
Valenzise G., Gerosa L., Tagliasacchi M., Antonacci F. and Sarti, A.(2007), "Scream and Gunshot Detection and Localization for Audio-Surveillance Systems," Proc. of IEEE Conference on Advanced Video and Signal Based Surveillance, London, UK.
William B. M. and Guin A.(2007), "Traffic Management Center use of Incident Detection Algorithms: Findings of a Nationwide Survey," IEEE Trans. on Intel. Trans. Sys., vol. 8, no. 2, pp.351-358.

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