Vehicle and pedestrian safety monitoring is an important task of urban traffic monitoring. Aiming at the problems of poor visual effect high noise and difficult target detection of the images collected under complex and bad weather conditions such as fog and haze a dual backbone network MobileNets VGG-DCBM Network MVNet was proposed for traffic object detection in fog. Inspired by the network structure of PCCN and CBNet this structure was composed of improved depthwise separable convolutional neural network MobileNets and VGG-DCBM network based on VGGNet. The dual backbone object detection network structure was constructed by using the parallel method. The improved MobileNets was the main backbone network and VGG-DCBM was the assistant backbone network to extract the feature information and realize the fusion of feature information between different networks. MVNet network structure adopted the parallel method to obtain the information of different feature layers extracted by the two networks and realized the fusion of different network feature information by using the method of channel splicing so as to obtain richer detailed features. On RTTS and HazePerson datasets the mean average precisions mAP reached 71. 50% and 89. 84% respectively. The experimental results show that this method has strong robustness in complex bad weather conditions such as fog and haze and can accurately detect vehicles and pedestrians. It is better than the comparison method in object detection performance.