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License Plate Location Method Based on Neural Network

Bp neural network is currently the most in-depth and widely used model in artificial neural networks. It is an application model of bp (backpropagation) learning algorithm in multilayer feedforward networks.

The essence of the bp network is a multilayer perceptron (map). For a commonly used three-layer (including input layer, hidden layer and output layer) network, the first layer belongs to the input layer and accepts input vectors; The second layer belongs to the hidden layer, which is used for memory and increases the adjustable parameters of the network to make the network output more accurate. The third layer belongs to the output layer and outputs the network result. Nodes between adjacent layers are fully connected, and nodes between the same layers are not connected. In theory, for a three-layer bp network, as long as the number of nodes in the hidden layer is increased to a certain range, any non-linear function can be fitted. The bp algorithm consists of forwarding and backward propagation. Forward propagation is when the input signal passes from the input layer to the output layer through the hidden layer. If the output layer gets the desired output, the algorithm ends; otherwise, go to backpropagation. Backpropagation is the backward calculation of the error signal (the difference between the sample output and the network output) according to the original connection path, and the weight of each layer of neurons is adjusted by the gradient descent method to reduce the error signal until the error reaches the desired error.