Large Crowd Count Based on Improved SURF Algorithm

This paper uses an analysis of Speeded up Robust Feature (SURF), based on the method of Linear Interpolation for camera distortion calibration, for high-density crowd counting. The eigenvalues are built on the Gray Level Co-occurrence Matrix (GLCM) features and the SURF features. Though the method of linear interpolation, weight values are interpolated to reduce the error, which is caused by camera distortion calibration. The optimized crowd’s feature vector can be got then. Through the method of support vector regression, the crowd’s number can be forecast by training model. The experiment result shows that the method of this paper has a higher accuracy than the previous methods

A Counting Method of Red Jujube Based on Improved YOLOv5s

Due to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which realized the fast and accurate detection of red jujubes and reduced the model scale and estimation error. ShuffleNet V2 was used as the backbone of the model to improve model detection ability and light the weight. In addition, the Stem, a novel data loading module, was proposed to prevent the loss of information due to the change in feature map size. PANet was replaced by BiFPN to enhance the model feature fusion capability and improve the model accuracy. Finally, the improved YOLOv5s detection model was used to count red jujubes. The experimental results showed that the overall performance of the improved model was better than that of YOLOv5s. Compared with the YOLOv5s, the improved model was 6.25% and 8.33% of the original network in terms of the number of model parameters and model size, and the Precision, Recall, F1-score, AP, and Fps were improved by 4.3%, 2.0%, 3.1%, 0.6%, and 3.6%, respectively. In addition, RMSE and MAPE decreased by 20.87% and 5.18%, respectively. Therefore, the improved model has advantages in memory occupation and recognition accuracy, and the method provides a basis for the estimation of red jujube yield by vision

Particulate matter pollution over China and the effects of control policies

China is one of the regions with highest PM(2.5)concentration in the world. In this study, we review the spatio-temporal distribution of PM2.5 mass concentration and components in China and the effect of control measures on PM2.5 concentrations. Annual averaged PM2.5 concentrations in Central-Eastern China reached over 100 mu g m(-3), in some regions even over 150 mu g m(-3). In 2013, only 4.1% of the cities attained the annual average standard of 35 mu g m(-3). Aitken mode particles tend to dominate the total particle number concentration. Depending on the location and time of the year, new particle formation (NPF) has been observed to take place between about 10 and 60% of the days. In most locations, NPF was less frequent at high PM mass loadings. The secondary inorganic particles (i.e., sulfate, nitrate and ammonium) ranked the highest fraction among the PM2.5 species, followed by organic matters (OM), crustal species and element carbon (EC), which accounted for 6-50%, 15-51%, 5-41% and 2-12% of PM2.5, respectively. In response to serious particulate matter pollution, China has taken aggressive steps to improve air quality in the last decade. As a result, the national emissions of primary PM2.5, sulfur dioxide (SO2), and nitrogen oxides (NOx) have been decreasing since 2005, 2006, and 2011, respectively. The emission control policies implemented in the last decade could result in noticeable reduction in PM2,(5)concentrations, contributing to the decreasing PM2.5 trends observed in Beijing, Shanghai, and Guangzhou. However, the control policies issued before 2010 are insufficient to improve PM2.5 air quality notably in future. An optimal mix of energy-saving and end-of-pipe control measures should be implemented, more ambitious control policies for NMVOC and NH3 should be enforced, and special control measures in winter should be applied. 40-70% emissions should be cut off to attain PM2.5 standard. (C) 2017 Elsevier B.V.All rights reserved.Peer reviewe