An automatic optical inspection system for the diagnosis of printed circuits based on neural networks

The aim of this work is to define a procedure to develop diagnostic systems for printed circuit boards, based on automated optical inspection with low cost and easy adaptability to different features. A complete system to detect mounting defects in the circuits is presented in this paper. A low-cost image acquisition system with high accuracy has been designed to fit this application. Afterward, the resulting images are processed using the wavelet transform and neural networks, for low computational cost and acceptable precision. The wavelet space represents a compact support for efficient feature extraction with the localization property. The proposed solution is demonstrated on several defects in different kind of circuits.