Traffic sign recognition method based on asymmetric convolution neural network
A convolutional neural network and traffic sign recognition technology, applied in the field of traffic sign recognition based on asymmetric convolutional neural network, can solve the problems of low recognition accuracy and slow recognition speed.
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[0058] 1, determine training set, what the present invention selects is the training set in GTSRB (Germany traffic sign recognition benchmark, German traffic sign recognition benchmark), comprises training picture 39,209 pieces, test picture 12630 pieces.
[0059] 2. Preprocess the pictures in the training set, take out the target area (traffic sign area) in the original image, convert the color image into a grayscale image, and scale it to 48×48, and then pass the target area through histeq image contrast Enhanced processing, the original training set is obtained, and the test set is processed in the same way. In order to make the trained model more robust, the original training set is rotated [-10°, 10°] and scaled [0.9, 1.1], and then added to the original data set to form a new training set. In the new Randomly select samples equivalent to the number of test sets from the dataset to form the validation set, and the remaining samples form the final training set.
[0060] 3...
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