Aiming at the problem that in harsh environments such as rain

snow

fog

etc

traffic signs are easily blocked

and the target is small

which is difficult to be accurately identified and accurately positioned

a strategy of coarse to fine（C to F） is proposed and an improvs cascade region with convolutional neural network features（R-CNN） network is adapted

which includes improved anchor design

online difficult case mining and multi-scale training.At the same time

the image enhancement is performed by image brightness enhancement and defogging algorithm.Finally

two different backbone network models are used for fusion.The results show that the proposed algorithm performs excellent generalization ability and accuracy in the dataset provided by the automatic driving traffic sign recognition competition based on virtual simulation environment and scored 0.9972 on the index F1.It can effectively overcome the interference factors such as different weather conditions and pedestrian conditions in virtual scenes and accurately identify the traffic signs around the road.