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

Claim 2:
3. The traffic sign recognition method based on a lightweight neural network according to claim 1, wherein 2nd to 5th layers of the convolution feature extraction part adopt the first separable asymmetric convolution, wherein a full line part of a residual connection mode indicates that the 1×1 convolution with a step length of 1 is adopted, and the number of the convolution kernels is 64; after the first separable asymmetric convolution of the 2nd to the 5th layers, a feature map with a length and a width of 64×64 and a channel of 64 is obtained;
a 6th layer of the convolution feature extraction part adopts the second separable asymmetric convolution, and after the second separable asymmetric convolution of the 6th layer, a feature map with a length and a width of 32×32 and a channel of 64 is obtained;
7th- to 11th layers of the convolution feature extraction part adopt the first separable asymmetric convolution, wherein a dotted line part of the residual connection mode indicates a 1×1 convolution with a step length of 2 is adopted, and the number of the convolution kernels is 64, while the full line part of the residual connection mode adopts a 1×1 convolution with a step length of 1, and the number of the convolution kernels is 64; after the first separable asymmetric convolution of the 7th- to 11th layers, a feature map with a length and width of 32×32 and a channel of 64 is obtained;
a 12th layer of the convolution feature extraction part adopts the second separable asymmetric convolution, and after the second separable asymmetric convolution of the 12th layer, a feature map with a length and width of 16×16 and a channel of 64 is obtained;
13th- to 15th layers of the convolution feature extraction part adopt the first separable asymmetric convolution, wherein the dotted line part of the residual connection mode indicates a 1×1 convolution with a step length of 2 is adopted, and the number of the convolution kernels is 64; the full line part of the residual connection mode adopts 1×1 convolution with step length of 1, and the number of the convolution kernels is 64; after that first separable asymmetric convolution of the 13th- to 15th layers, a feature map with a length and a width of 16×16 and a channel of 64 is obtained;
a 16th layer of the convolution feature extraction part adopts the second separable asymmetric convolution; after the second separable asymmetric convolution of the 16th layer, a feature map with a length and a width of 8×8 and a channel of 64 is obtained;
a 17th layer of the convolution feature extraction part adopts the first separable asymmetric convolution; wherein the dotted line part of the residual connection mode indicates that a 1×1 convolution with a step length of 2 is adopted, and the number of the convolution kernels is 64; after the first separable asymmetric convolution of the 17th layer, a feature map with a length and a width of 8×8 and a channel of 64 is obtained.