| In recent years,the number of motor vehicles in my country has increased rapidly,and traffic accidents have occurred frequently.Due to its special driving environment,the fatality rate of traffic accidents on mountain roads is higher than that on plain roads and urban roads,and the occurrence of these accidents is related to the misjudgment and omission of traffic signs by drivers.Therefore,it is of great practical significance to study the identification technology of traffic signs on mountain highways to ensure the safety of drivers on mountain highways and to reduce the occurrence of mountain highway traffic accidents.This paper takes the mountain road traffic sign images as the research object,and on the basis of summarizing and analyzing the traffic sign recognition methods and research status at home and abroad,analyzes and studies the characteristics and problems in the identification of mountain road traffic signs;due to the lack of mountain road traffic sign datasets in China,this paper made a mountain road traffic sign dataset by manual annotation,and performed morphological operations on the traffic signs to expand the dataset;due to the special environment of mountain roads,the traffic sign images of mountain roads are easily disturbed by light and weather factors,this paper uses an improved Retinex algorithm to improve the brightness of traffic sign images,enhance the contrast of images,and highlight the characteristics of traffic signs;in view of the low recognition efficiency of the original YOLOv4 model,which cannot meet the real-time requirements of road traffic sign recognition in mountainous areas,this paper introduces the lightweight network MobileNetv3 as the backbone feature extraction network,and replaces the standard convolution in PANet with a depthwise separable convolution to reduce the amount of parameters and computation of the model,the traffic signs are clustered to obtain a priori frame size template that conforms to the current data set,which improves the recognition efficiency of the model;in order to solve the problem of missed detection and false detection of small-sized traffic signs in mountain highway images,the PANet structure of YOLOv4’s feature pyramid path aggregation network is improved,and the scale of the prediction network is increased from 3 to 4,at the same time,the localization loss of the model is designed,which improved the overall recognition accuracy of the model;Combined with the previous traffic sign recognition model,according to the front-end and back-end interaction framework,the development of the mountain highway traffic sign recognition system is completed,and the system function is tested,the test results show that the method in this paper can meet the practical application requirements of mountain highway traffic sign recognition in accuracy and real-time performance.This paper realizes the accurate and rapid identification of traffic signs in mountain highway scenes through the research on the recognition of traffic signs on mountain highways,which is of great significance to ensure the driving safety of drivers and promote the commercial implementation of assisted driving systems,and has certain theoretical research significance and engineering application value. |
