Driver abnormal behavior detection based on dual-channel attention mechanism

In order to effectively detect the abnormal driving behavior of drivers and reduce the incidence of traffic accidents, a YOLOv3 abnormal behavior monitoring algorithm based on dual channel attention mechanism is proposed. The algorithm is based on YOLOv3 network model. Firstly, the K-mean clustering algorithm is used to recluster the anctor, and nine prior frames suitable for abnormal behavior are obtained. Then, a dual channel attention mechanism module is introduced into the YOLOv3 feature extraction network to increase the weight of important information in each channel of the feature map. Finally, the loss function of the model is improved. Giou loss is used as the loss function of the boundary box of the model to better realize the regression of the boundary box and improve the performance and accuracy of the model. The experimental results show that the map value of the improved YOLOv3 algorithm in driver abnormal behavior detection reaches 95.91%, which is much higher than the traditional YOLOv3 model and other target detection models, and the detection effect is remarkable.