The invention discloses a
traffic sign classification method based on a deep neural network. The
traffic sign classification method comprises the following steps: A, detecting a read-in
video based on a moving
object detection method of a light
stream method, and when a moving object is detected, extracting a
region of interest; B, utilizing blocks with fixed sizes to carry out blocked
processing on the extracted
region of interest; C, carrying out zooming
processing on pictures after the blocked
processing, and converting into pictures with the same size; D, inputting the converted pictures, and utilizing a
convolutional neural network for classification. According to the method, the
region of interest is extracted for the pictures after
motion detection and then the blocked processing is carried out, and after the obtained pictures are converted into the pictures with the same size, processing is carried out by utilizing the
convolutional neural network, so that the problems caused by an artificially assumed class conditional density function are avoided, the testing speed is greatly quickened, and the precision is greatly improved. The
traffic sign classification method based on the deep neural network disclosed by the invention can be widely applied to the traffic field.