Abstract
We propose a high-performance algorithm while using a promoted and modified form of the You Only Look Once (YOLO) model, which is based on the TensorFlow framework, to enhance the real-time monitoring of traffic-flow problems by an intelligent transportation system. Real-time detection and traffic-flow statistics were realized by adjusting the network structure, optimizing the loss function, and introducing weight regularization. This model, which we call YOLO-UA, was initialized based on the weight of a YOLO model pre-trained while using the VOC2007 data set. The UA-CAR data set with complex weather conditions was used for training, and better model parameters were selected through tests and subsequent adjustments. The experimental results showed that, for different weather scenarios, the accuracy of the YOLO-UA was ~22% greater than that of the YOLO model before optimization, and the recall rate increased by about 21%. On both cloudy and sunny days, the accuracy, precision, and recall rate of the YOLO-UA model were more than 94% above the floating rate, which suggested that the precision and recall rate achieved a good balance. When used for video testing, the YOLO-UA model yielded traffic statistics with an accuracy of up to 100%; the time to count the vehicles in each frame was less than 30 ms and it was highly robust in response to changes in scenario and weather.
Highlights
The continually rising number of vehicles on roads has increased the incidence of traffic accidents and traffic congestion, with serious negative effects on people’s transportation experiences and overall lives
(GIOU) metric to optimize the loss function directly, and we show that the You Only Look Once (YOLO) model can be metric and the Generalized Intersection Over Union (GIOU) metric to optimize the loss function modified and promoted to enhance traffic flow monitoring [31,32]
The method of fine-tuning the directly, and we show that the YOLO model can be modified and promoted to enhance traffic flow model structure and the GIOU optimization loss function is proposed to enhance the accuracy of the monitoring [31,32]
Summary
The continually rising number of vehicles on roads has increased the incidence of traffic accidents and traffic congestion, with serious negative effects on people’s transportation experiences and overall lives. In order to improve the detection speed and accuracy, based on the DenseNet model [23], the researchers proposed a lightweight PeleeNet model for mobile devices, which had the highest target classification accuracy [24] Their further study had combined the PeleeNet and optimized SSD (Single Shot MultiBoxDetector) to develop a real-time target detection system for mobile devices, which had low computational cost and reliable targets’ detection performance [24,25]. We present YOLOproblem, we need an optimized model, with an algorithm that is fast and uses relatively few UA, which is a regression-based, high-performance algorithm for real-time detection and statistics calculations.
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