Patent ID: 11875576
Assignee: QUANZHOU EQUIPMENT MANUFACTURING RESEARCH INSTITUTE
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 4:
5. The traffic sign recognition method based on a lightweight neural network according to claim 1, wherein a first layer of separable full connection module of the classifier part firstly converts a feature map of a previous layer with a length and a width of 8×8 and a channel of 64 into a shape of 64×64, and then initializes two weight matrixes with sizes of 64×64 respectively, namely A-1 and B-1, and then, performs a matrix multiplication with a matrix A-1 and an input after a dimension conversion, and an obtained result is matrix multiplied with the matrix B-1 to obtain an output matrix with a size of 64×64 of a next layer;
a second layer of separable full connection module firstly respectively initializes two weight matrixes with sizes of 64×64, namely A-2 and B-2, and finally a matrix A-2 is used to perform the matrix multiplication with an output matrix of a previous layer with a size of, and then an obtained result is matrix multiplied with the matrix B-2 to obtain an output matrix with a size of 64×64 of a next layer;
a third layer of separable full connection module firstly respectively initializes two weight matrixes with sizes of 1×64 and 64×64, namely A-3 and B-3, and then performs the matrix multiplication with an output matrix with a size of 64×64 of a previous layer with a matrix A-3, and then an obtained result performs the matrix multiplication with the matrix B-3 to obtain an output matrix with a size of 1×64 of a next layer; finally, the output matrix is flattened after a Flatten operation, and a softmax activation function is used to recognize 64 categories of traffic signs.