基于改进长短时记忆神经网络-自适应增强算法的多天气车辆分类方法

In view of the poor results of existing domestic and oversea vehicle classification schemes and relatively significant effects of climate on them, a multi-climate vehicle classification method based on modified LSTM-AdaBoost (long short-term memory neural network-Adaptive boosting) algorithm is proposed, and a "multi-layer grid method" is also put forward to accurately determine the hyperparameters of LSTM. Firstly, the geomagnetic vehicle detection system and vehicle classification method are established. Then the results of vehicle classification based on modified LSTM-AdaBoost are analyzed, and the classification accuracies of different vehicle classification methods and different climates are compared. The results show that compared with K-nearest neighbor and BP neural network algorithms for classification, the proposed method has higher accuracy with a highest classification accuracy of 92.2%. Among three climates of torrential rain, haze and fine day, the classification accuracy in torrential rain is lowest, but the difference is rather small, 3.9 percentage points at most.