Abstract
Today's society has seen a sharp rise in the number of accidents caused by drivers failing to pay attention to traffic signals and regulations. Road accidents are increasing daily as the number of automobiles rises. By using synthesis data for training, which are produced from photos of road traffic signs, we are able to overcome the challenges of traffic sign identification and decrease violations of traffic laws by identifying triple-riding, no-helmet, and accidents, which vary for different nations and locations. This technique is used to create a database of synthetic images that may be used in conjunction with a convolution neural network (CNN) to identify traffic signs, triple riding, no helmet use, and accidents in a variety of view lighting situations. As a result, there will be fewer accidents, and the vehicle operator will be able to concentrate more on continuing to drive but instead of checking each individual road sign. Also, simplifies the process to recognize triple driving, accidents, but also incidents when a helmet was not used.
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