摘要
In recent years,the problem of“Impolite Pedestrian”in front of the zebra crossing has aroused widespread concern from all walks of life.The traffic sector’s governance measures have become more serious.The traditional way of governance is on-site law enforcement,which requires a lot of manpower and material resources and is low efficiency.An enhanced YOLOv3-tiny model is proposed for pedestrians and vehicle detection in traffic monitoring.By modifying the backbone network structure of YOLOv3-tiny model,introducing deep detachable convolution operation,and designing the basic residual block unit of the network,the feature extraction ability of the backbone network is enhanced.The improved model is trained on the VOC2007+VOC2012 training set,and the trained model is tested for performance on the test data set.The experimental results show that:the mean Average Precision(mAP)increased from 0.672 to 0.732,increasing the measurement accuracy by 9%.The Intersection over Union(IoU)increased from 0.783 to 0.855,increasing the coverage accuracy by 7.2%.The enhanced YOLOv3-tiny model has higher measurement accuracy than the original model.Applying this model to the 1080P traffic video on the NVIDIA RTX 2080,the detection speed is 150 FPS,which can fully achieve real-time detection.Through the analysis of pedestrians and vehicle coordinates,it is judged whether or not illegal acts occur.For illegal vehicles,save three pictures as the basis for law enforcement,which forms an important supplement to off-site law enforcement.
基金
supported by the following funds:National Key R&D Program of China(2018YFF01010100)
National natural science foundation of China(61672064)
Beijing natural science foundation project(4172001)
Advanced information network Beijing laboratory(PXM2019_014204_500029).
