Accurate Vision-Based PCB Positioning Using Cosine-Convolutional Neural Networks

Object positioning is a fundamental task for assembly and inspection operations in the electronic industry. Traditional methods such as template matching and feature-point detection have been applied for object positioning. They are however computationally expensive and are generally affected by environmental changes. Deep learning models based on Convolutional Neural Network (CNN) have also been used for object positioning. They are computationally very efficient but can be sensible under environmental variations such as illumination and noise. In this article, a Cosine-Convolution operation is proposed with the objective of minimizing the effects of illumination variations. The proposed Cosine-Convolution can substitute any convolutional operation in a CNN-based regressor for positioning tasks. The proposed convolution is based on cosine-measure that normalizes the convolution between an image window and a filter at local scope. The proposed Cosine-Convolutional Neural Network (Cosine-CNN) performs more accurate than the traditional CNN under environment variations, including illumination changes, noise, defocusing and template occlusion. The experimental results reveal that the proposed model can reach mean prediction errors smaller than 1 pixel and 1. for object shift and rotation, regardless of the environmental variations. The method is computationally very fast, with an average evaluation time of 1.2-ms for real-time PCB positioning.