Abstract
Faster R-CNN architecture is used to solve the problems of moving path uncertainty, changeable coverage, and high complexity in cold-air induced large-scale intensive temperature-reduction (ITR) detection and classification, since those problems usually lead to path identification biases as well as low accuracy and generalization ability of recognition algorithm. In this paper, an improved recognition method of national ITR (NITR) path in China based on faster R-CNN in complicated meteorological systems is proposed. Firstly, quality control of the original dataset of strong cooling processes is carried out by means of data filtering. Then, according to the NITR standard and the characteristics of NITR, the NITR dataset in China is established by the intensive temperature-reduction areas located through spatial transformation. Meanwhile, considering that the selection of regularization parameters of Softmax classification method will cause the problem of probability calculation, support vector machine (SVM) is used for path classification to enhance the confidence of classification. Finally, the improved faster R-CNN model is used to identify, classify, and locate the path of NITR events. The experimental results show that, compared to other models, the improved faster R-CNN algorithm greatly improves the performance of NITR’s path recognition, especially for the mixed NITR paths and single NITR paths. Therefore, the improved faster R-CNN model has fast calculation speed, high recognition accuracy, good robustness, and generalization ability of NITR path recognition.
Highlights
China, located in the east of Eurasian continent and adjacent to the Northwest Pacific, is significantly influenced by the prominent Asia monsoon system originating from the thermal gradient between ocean and land [1]
The nonlinear problem can be transformed into a linear problem in a high-dimensional space through space transformation, and the optimal classification surface or the optimal generalized classification surface can be obtained in the transformed highdimensional space. e kernel function is used to map the linear nonseparable low dimensional space to the linear separable high-dimensional space. e common kernel functions are the Polynomial function, the Radial Basis function (RBF), and the Sigmoid function
To objectively evaluate the generalization ability of the national ITR (NITR) path type recognition model, the average precision (AP) and mean average precision (mAP) criteria are used as measurements of derivation between observed and predicted values
Summary
China, located in the east of Eurasian continent and adjacent to the Northwest Pacific, is significantly influenced by the prominent Asia monsoon system originating from the thermal gradient between ocean (the Pacific and Indian Ocean) and land (the Asia continent) [1]. In January of 2008, most areas of southern China suffered an extreme cold spell accompanied by severe precipitation and snowfall [7, 8], which brought grave traffic and energy pressure. Such cold wave happened in the Spring Festival travel season and many people had to stay in railway stations or airports for several days and could not come back home. Under the background of global warming, subtropical extreme cold events keep increasing rather than decreasing because of the weakened westerly jet associated with the lessened temperature gradient between polar and tropical regions [9, 10], and CA events become a hot topic
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