Abstract
This study proposed a model for highway accident detection that combines the You Only Look Once v3 (YOLOv3) object detection algorithm and Canny edge detection algorithm. It not only detects whether an accident has occurred in front of a vehicle, but further performs a preliminary classification of the accident to determine its severity. First, this study established a dataset consisting of around 4500 images mainly taken from the angle of view of dashcams from an open-source online platform. The dataset was named the Highway Dashcam Car Accident for Classification System (HDCA-CS) and was developed with the aim of conforming to the setting of this study. The HDCA-CS not only considers weather conditions (rainy days, foggy days, nighttime settings, and other low-visibility conditions), but also various types of accidents, thus increasing the diversity of the dataset. In addition, we proposed two types of accidents—accidents involving damaged cars and accidents involving overturned cars—and developed three different design methods for comparing vehicles involved in accidents involving damaged cars. Canny edge detection algorithm processed single high-resolution images of accidents were also added to compensate for the low volume of accident data, thereby addressing the problem of data imbalance for training purposes. Lastly, the results showed that the proposed model achieved a mean average precision (mAP) of 62.60% when applied to the HDCA-CS testing dataset. When comparing the proposed model with a benchmark model, two abovementioned accident types were combined to allow the proposed model to produce binary classification outputs (i.e., non-occurrence and occurrence of an accident). The HDCA-CS was then applied to the two models, and testing was conducted using single high-resolution images. At 76.42%, the mAP of the proposed model outperformed the benchmark model’s 75.18%; and if we were to apply the proposed model to only test scenarios in which an accident has occurred, its performance would be even better relative to the benchmark. Therefore, our findings demonstrate that our proposed model is superior to other existing models.
Highlights
The results demonstrated that the mean average precision of the proposed model, following tests based on dashcam images, was 62.60%; and when single high-resolution images were used, the model’s mAP reached up to 72.08%
While YOLO-CA does not enhance the detection of medium-sized objects, the Canny edge detection algorithm used in our study enables data enhancement; that is, training is carried out to achieve enhanced identification with respect to the relevant objects found in single high-resolution images of car accidents
We proposed an efficient deep learning model for highway accident detection and classification that combines the You Only Look Once v3 (YOLOv3) object detection algorithm and Canny edge detection algorithm
Summary
The rapidly developing technology of deep learning has been widely applied in recent years. As a novel field of study, deep learning seeks to analyze and interpret data by mimicking and simulating the way in which the human brain operates. The core aspect of deep learning is to train multilayer neural network models using large volumes of data to determine input-output relationships. The number of layers in a model, the number of neurons in each layer, how the neurons are connected, and how the functions are simulated and determined based on the problem at hand.
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