基于LSTM网络的前车行为识别OA

To solve the problems of traditional method with poor practicability and low recognition rate for the front vehicle behavior,the new recognition method based on LSTM network model was proposed.In the process of vehicle detection,due to the difficulty in extracting features of vehicle targets and occluded targets in remote scenes,the phenomenon of wrong detection and missing detection occurred.The vehicle detection algorithm YOLOv5-BA improved by YOLOv5 in complex background was adopted.The feature fusion idea of weighted bidirectional feature pyramid network(BiFPN)was introduced,and the adaptive feature fusion module ASFF was introduced in the detection part to improve the detection performance.The results show that the high detection accuracy of the improved algorithm reaches 97.3％on KITTI data set.On this basis,combined with DeepSort algorithm,the vehicle detection and tracking are realized.By the forward vehicle behavior recognition model based on LSTM network,the average accuracy of the model on the data set is 92.3％,which can be used in the practical application of vehicle behavior recognition.