基于卷积循环神经网络的芯片表面字符识别

熊帆,陈田,卞佰成,刘军

Chip surface character recognition based on convolutional recurrent neural network

Fan XIONG,Tian CHEN,Bai-cheng BIAN,Jun LIU

表 1 改进后的CNN模块结构

Tab.1 Improved CNN module structure

类型 描述 激活函数 卷积参数 Input 3×W×32 − − Conv Layer 1 3→32 h-swish k: (3,3)s: (2,2)p: (1,1) Conv Block 1 32→32 relu k:(1,1)-(3,3)-(1,1)s:(1,1)-(1,1)-(1,1)p:(0,0)-(1,1)-(0,0) Conv Block 2 32→48 relu k:(1,1)-(3,3)-(1,1)s:(1,1)-(2,2)-(1,1)p:(0,0)-(1,1)-(0,0) Conv Block 3 48→48 relu k:(1,1)-(3,3)-(1,1)s:(1,1)-(1,1)-(1,1)p:(0,0)-(1,1)-(0,0) Conv Block 4 48→64 h-swish k:(1,1)-(3,3)-(1,1)s:(1,1)-(2,2)-(1,1)p:(0,0)-(1,1)-(0,0) Conv Block 5 64→96 h-swish k:(1,1)-(3,3)-(1,1)-(1,1)s:(1,1)-(1,1)-(1,1)-(1,1)p:(0,0)-(1,1)-(0,0)-(0,0) Conv Block 6 96→128 h-swish k:(1,1)-(3,3)-(1,1)-(1,1)s:(1,1)-(1,1)-(1,1)-(1,1)p:(0,0)-(1,1)-(0,0)-(0,0) Conv Block 7 128→256 h-swish k:(1,1)-(3,3)-(1,1)s:(1,1)-(2,1)-(1,1)p:(0,0)-(1,1)-(0,0) Conv Block 8 256→256 h-swish k:(1,1)-(3,3)-(1,1)s:(1,1)-(1,1)-(1,1)p:(0,0)-(1,1)-(0,0) Conv Layer 2 256→512 h-swish k:(2,2) s:(1,1)p:(0,0) Output 512×1×40 − −