Evaluation of the Accuracy of Genetic Algorithms in Orientation Estimation of Objects in Industrial Environment

The world of machine vision and robotic vision revolve around a wide array of techniques and methodologies built around image processing. While conceiving and developing any such technique, due importance is accorded to the nature of the problem to be addressed and the ultimate purpose to be realized. For instance, realizing object recognition in an image environment having multiple objects, realizing object detection and recognition in an image environment characterized by occlusion and clutter and many other unique scenarios like these. For realizing object detection, localization eventually leading to object recognition in environments characterized by multiple objects and occlusion and the environments having objects undergoing different rotations in an image plane; a proper estimate of object orientation is central to an appropriate pose estimation of object which in turn plays a vital role in accurate recognition of the object. For realizing object recognition in such unique scenarios, orientation and pose estimation will go hand in hand. In the present study, an attempt is made to accurately estimate the orientation of a single industrial object in an image using genetic algorithm (GA), a nature inspired evolutionary technique for optimization and thus facilitates in evaluating the potential and accuracy of the GA when used as a standalone GA in providing a correct orientation estimate. The analysis of the results presents the GA as a reliable, potent and efficient tool for suitably estimating the orientation of the single object in the image.