Automatic Hybrid Genetic Algorithm Based Printed Circuit Board Inspection

First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06) Pub Date : 2006-06-15 DOI:10.1109/AHS.2006.28

S. Mashohor, J. Evans, A. Erdogan

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引用次数: 17

Abstract

The paper presents a novel integrated system in which a number of image processing algorithm are embedded within a genetic algorithm (GA) based framework in order to provide an adaptation and better quality analysis with less computational complexity while maintaining flexibility to a broad range of defects. A specially tailored hybrid GA (HGA) is used to estimate geometric transformation of arbitrarily placed printed circuit boards (PCBs) on a conveyor belt without any prior information such as CAD data. A library of image processing functions is accessed by the HGA within an intelligent framework. These functions include operations such as fixed multi-thresholding, Sobel edge-detection, image subtraction and noise filters. The proposed framework allows novel composition of tasks such as edge-detection and thresholding in order to increase defect detection accuracy with low computational time. Our simulations on real PCB images demonstrate that the HGA is robust enough to detect any missing components and cut solder joint with any size and shape with significant reduction in computational time compared to conventional approaches

查看原文本刊更多论文

基于自动混合遗传算法的印刷电路板检测

本文提出了一种新的集成系统，该系统将许多图像处理算法嵌入到基于遗传算法(GA)的框架中，以便以更少的计算复杂度提供适应性和更高质量的分析，同时保持对广泛缺陷的灵活性。采用一种特殊的混合遗传算法(HGA)来估计任意放置在传送带上的印刷电路板(pcb)的几何变换，而不需要任何CAD数据等先验信息。HGA在智能框架内访问图像处理函数库。这些功能包括固定多阈值分割、索贝尔边缘检测、图像减法和噪声滤波等操作。提出的框架允许新的任务组合，如边缘检测和阈值，以提高缺陷检测精度和低计算时间。我们对真实PCB图像的模拟表明，与传统方法相比，HGA具有足够的鲁棒性，可以检测任何缺失的组件和切割任何尺寸和形状的焊点，并且大大减少了计算时间

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06)

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