In the intelligent traffic system, real-time and accurate detections of vehicles in images and video data are very important and challenging work. Especially in situations with complex scenes, different models, and high density, it is difficult to accurately locate and classify these vehicles during traffic flows. Therefore, we propose a single-stage deep neural network YOLOv3-DL, which is based on the Tensorflow framework to improve this problem. The network structure is optimized by introducing the idea of spatial pyramid pooling, then the loss function is redefined, and a weight regularization method is introduced, for that, the real-time detections and statistics of traffic flows can be implemented effectively. The optimization algorithm we use is the DL-CAR data set for end-to-end network training and experiments with data sets under different scenarios and weathers. The analyses of experimental data show that the optimized algorithm can improve the vehicles’ detection accuracy on the test set by 3.86%. Experiments on test sets in different environments have improved the detection accuracy rate by 4.53%, indicating that the algorithm has high robustness. At the same time, the detection accuracy and speed of the investigated algorithm are higher than other algorithms, indicating that the algorithm has higher detection performance.

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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.

The kidney is an important organ in the regulation of blood pressure, and it is also one of the primary target organs of hypertension. Kidney damage in response to hypertension eventually leads to renal insufficiency. The authors previously demonstrated that vaccarin exhibits a protective role in endothelial injury. However, the effects of vaccarin on the two-kidney, one clip (2K1C) renovascular hypertension model and subsequent kidney injury have yet to be fully elucidated. The present study was designed to investigate the roles and mechanisms of vaccarin in attenuating hypertension and whether vaccarin had beneficial effects on kidney injury. The 2K1C rats had greater fibrosis, apoptosis, reactive oxygen species production, inflammation, angiotensin II (Ang II) and angiotensin type 1 (AT1) receptors in the right kidney compared with normotensive rats, which were alleviated by a high dose of vaccarin and captopril. Vaccarin treatment attenuated hypertension, reduced fibrosis markers, NADPH oxidase (NOX)-2, NOX-4, 3-nitrotyrosine, tumor necrosis factor-α, interleukin 1β (IL-1β), and IL-6 protein levels and altered pro-apoptotic protein levels including caspase-3, anti-apoptosis protein B cell lymphoma (Bcl)-2 and Bcl-2 associated X, apoptosis regulator in the right kidney of 2K1C rats. These findings suggest that the protective effects of vaccarin on the right kidney in renovascular hypertension are possibly due to downregulation of fibrosis, inflammatory molecules, oxidative stress, Ang II, and AT1 receptor levels.