A fast electronic components orientation and identify method via radon transform

This paper presents a method which combined radon transform with machine learning technology for electronic component orientation and identification in product line scenes. This method can fetch electronic components´ positions and yawing angles and enables the full automation of electronic product line. Firstly, it take images contain a single electronic component as training samples and retrieve its features. Secondly, it uses thresholds to segment objects in overlapping status. Finally, it use radon transform to detect the axis of object and then according to the component features acquired from training sample and classifier, the algorithm can identify electronic component pin´s orientation. To increase the method´s detection accuracy and speed in factory product line environment, this paper also proposed a strategy for the combination of the method with mechanism. Experiments show that this method has a perfect performance and completely fulfills the requirements of factory product line environment. This method achieves a recall rate of 81.7% and precision rate of 95.1%, after combined the algorithm with mechanism, the precision rate enhance to 98.5% and detection speed lifting strikingly.