지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.2
- 수록면
- 150 - 161 (12page)
- DOI
- 10.7840/kics.2023.48.2.150
이용수
초록· 키워드
오류제보하기
In the traffic industry, the automatic accident detection system is a major concern. Although image-based and radar-based traffic accident detection systems are commonly employed, they have several drawbacks, including the need to secure the camera’s field of view, a high rate of false alarms, and a lengthy detection time. Using a real-time acoustic surveillance system and the classification algorithm via Convolutional Neural Network (CNN), this article proposes several methods for identifying abnormal situations, such as a car crash or tire skid sound, to overcome the limitations of existing methods. We create an audio database by collecting sounds from two tunnels in South Korea using self-made microphones for eight months and classifying them into three categories: car crash, tire skid, and normal environmental sounds. We establish a three-step classification procedure using an algorithm. We compare the detection rate and false alarm rate of our proposed method to those of deep learning techniques including MLP (Multi-Layer Perceptron), Long-Short Term Memory, ShuffleNetv2, and MobileNetv2. In addition, we present a method for filtering out irrelevant sound data to improve the computational efficiency of our approach.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Dataset
Ⅲ. Proposed Methods
Ⅳ. Experiments
Ⅴ. Results
Ⅵ. Conclusion
References
참고문헌 (34)
참고문헌 신청
J. Kim, “Vehicle detection using deep learning technique in tunnel road environments,” Symmetry, vol. 12, no. 12, 2020. (https://doi.org/10.3390/sym12122012)
J. Baek, J. Min, S. Namkoong, and S. Yoon, “An in-tunnel traffic accident detection algorithm using cctv image processing,” KIPS Trans. Softw. and Data Eng., vol. 4, no. 2, pp. 83-90, 2015. (https://doi.org/10.3745/KTSDE.2015.4.2.83)
K. B. Lee and H. S. Shin, “An application of a deep learning algorithm for automatic detection of unexpected accidents under bad cctv monitoring conditions in tunnels,” in IEEE Int. Conf. Deep Learn. and Mach. Learn. in Emerging Appl. (Deep-ML), pp. 7-11,
Y.-D. Kim, G.-J. Son, C.-H. Song, and H.-K. Kim, “On the deployment and noise filtering of vehicular radar application for detection enhancement in roads and tunnels,” Sensors, vol. 18, no. 3, 837, 2018. (https://doi.org/10.3390/s18030837)
Y.-D. Kim, G.-J. Son, H. Kim, C. Song, and J.-H. Lee, “Smart disaster response in vehicular tunnels: Technologies for search and rescue applications,” Sustainability, vol. 10, no. 7, 2509, 2018. (https://doi.org/10.3390/su10072509)
J. Baek, J. Min, S. Namkoong, and S. Yoon, “An in-tunnel traffic accident detection algorithm using cctv image processing,” KIPS Trans. Softw. and Data Eng., vol. 4, no. 2, pp. 83-90, 2015. (https://doi.org/10.3745/KTSDE.2015.4.2.83)
K. B. Lee and H. S. Shin, “An application of a deep learning algorithm for automatic detection of unexpected accidents under bad cctv monitoring conditions in tunnels,” in IEEE Int. Conf. Deep Learn. and Mach. Learn. in Emerging Appl. (Deep-ML), pp. 7-11,
Y.-D. Kim, G.-J. Son, C.-H. Song, and H.-K. Kim, “On the deployment and noise filtering of vehicular radar application for detection enhancement in roads and tunnels,” Sensors, vol. 18, no. 3, 837, 2018. (https://doi.org/10.3390/s18030837)
Y.-D. Kim, G.-J. Son, H. Kim, C. Song, and J.-H. Lee, “Smart disaster response in vehicular tunnels: Technologies for search and rescue applications,” Sustainability, vol. 10, no. 7, 2509, 2018. (https://doi.org/10.3390/su10072509)
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