Research on PCB defect detection algorithm based on mixed attention mechanism

The production process of PCB is complex and cumbersome, which leads to the complex and diverse defects of the PCB and seriously affects the productivity. Therefore, timely and effective detection of its surface defects is particularly momentous. For this article, a new mixed attention module C_Efficient Channel Attention (C_ECA) is presented by connecting spatial attention with effective Channel attention. A one-dimensional convolution can be substituted for the fully connected layer in the channel attention module of C_ECA; for the spatial attention module of C_ECA, we can use 3*3 and 7*7 convolution kernel in place of the 7*7 convolution kernel to use multiple scale information to consider the significance of diverse regional trait. We integrated the module into YOLOv3 and tested it on PCB data set. Finding in the laboratory indicate that the network performance can be greatly improved and PCB defects can be accurately identified.