Traffic Sign Recognition Method in Complex Scenes Based on Improved YOLOv5
Nowadays traffic signs recognition is an inevitable and important part of intelligent driving,which is related to the safety of people using intelligent driving.Therefore,this paper takes the traffic signs in complex environment as the research object,aiming at the problem that the current traffic signs recognition is difficult to take into account the real-time and accuracy,and proposes an improved YOLOv5 traffic signs recognition algorithm.First,preprocess and enhance the data set to strengthen the ability of target detection.Then,the PP-LCNet lightweight network is used to reduce the parameters of the backbone network,realize the lightweight of the model,integrate the attention mechanism in the neck network,and enhance the ability of feature extraction.Experiments show that compared with the original YOLOv5s model,the model parame-ters of this algorithm are reduced by 25.9％;the detection speed is increased by 50.08 frames/s,and the aver-age accuracy is 97.58％.It is easy to deploy and can meet the real-time and accuracy requirements of traffic signs recognition in intelligent driving scenes.
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