Paper Info
Title
An Automatic Car Accident Detection Method Based on Cooperative Vehicle Infrastructure Systems
Abstract
Car accidents cause a large number of deaths and disabilities every day, a certain proportion of which result from untimely treatment and secondary accidents. To some extent, automatic car accident detection can shorten response time of rescue agencies and vehicles around accidents to improve rescue efficiency and traffic safety level. In this paper, we proposed an automatic car accident detection method based on Cooperative Vehicle Infrastructure Systems (CVIS) and machine vision. First of all, a novel image dataset CAD-CVIS is established to improve accuracy of accident detection based on intelligent roadside devices in CVIS. Especially, CAD-CVIS is consisted of various kinds of accident types, weather conditions and accident location, which can improve self-adaptability of accident detection methods among different traffic situations. Secondly, we develop a deep neural network model YOLO-CA based on CAD-CVIS and deep learning algorithms to detect accident. In the model, we utilize Multi-Scale Feature Fusion (MSFF) and loss function with dynamic weights to enhance performance of detecting small objects. Finally, our experiment study evaluates performance of YOLO-CA for detecting car accidents, and the results show that our proposed method can detect car accident in 0.0461 seconds (21.6FPS) with 90.02% average precision (AP). In additionally, we compare YOLO-CA with other object detection models, and the results demonstrate the comprehensive performance improvement on the accuracy and real-time over other models.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2939532
IEEE ACCESS
Keywords
DocType
Volume
Car accident detection,CVIS,machine vision,deep learning
Journal
7
ISSN
Citations 
PageRank 
2169-3536
1
0.35
References 
Authors
0
4
Name
Order
Citations
PageRank
Daxin Tian120432.49
Chuang Zhang288.67
Xuting Duan3264.92
Xixian Wang410.35