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.
Highlights
According to the World Health Organization, there are about 1.35 million deaths and 20-50 million injuries as a result of the car accident globally every year [1]
The car accident detection application program with YOLO-CA model is deployed on the edge server, which is developed based on CAD-Cooperative Vehicle Infrastructure Systems (CVIS) and deep learning algorithms
In this paper, we have proposed an automatic car accident detection method based on CVIS
Summary
According to the World Health Organization, there are about 1.35 million deaths and 20-50 million injuries as a result of the car accident globally every year [1]. It is vital important to develop an efficient accident detection method, which can significantly reduce both the number of deaths and injuries as well as the impact and severity of accidents [5]. Tian et al.: Automatic Car Accident Detection Method Based on CVIS deep-learning into methods of car accident detection These methods extract and process complex image features instead of single vehicle motion parameter, which improves the accuracy of detecting car accidents. We propose a data-driven car accident detection method based on CVIS, whose goal is improving efficiency and accuracy of car accident response. We build a novel dataset, Car Accident Detection for Cooperative Vehicle Infrastructure System dataset (CAD-CVIS), which is more suitable for car accident detection based on roadside intelligent devices in CVIS.
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