7 Ankit Sharma, Raminder Preet Pal Singh, and Parveen Lehana, “Evaluation of the accuracy of genetic algorithms in orientation estimation of objects in industrial environment,” International Journal of Scientific and Technical Advancements, Volume 1, Issue 4, pp. 7-14, 2015. International Journal of Scientific and Technical Advancements ISSN: 2454-1532 Evaluation of the Accuracy of Genetic Algorithms in Orientation Estimation of Objects in Industrial Environment Ankit Sharma 1 , Raminder Preet Pal Singh 2 , Parveen Lehana 3 1 Electrical Engineering Department, Model Institute of Engineering and Technology, Jammu, Jammu & Kashmir, India-181122 2 Electronics and Communication Engineering Department, Arni University, Kathgarh, Himachal Pradesh, India-176401 3 Department of Physics and Electronics, University of Jammu, Jammu, Jammu & Kashmir, India-180006 E-mail: 1 svankit@yahoo.co.in, 2 raminder_212003@rediffmail.com, 3 pklehana@gmail.com. Abstract—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. Keywords— Object detection; object orientation; robotic vision; image segmentation; image thresholding; genetic algorithm; selection; mutation. I. INTRODUCTION bject detection and recognition are indispensable and integral to the domains of machine vision and robotic vision systems [1]. The functional quality of robotic vision systems employed in both industrial robots for industrial automation and service robots for household applications is greatly influenced not only by the hardware involved but also by the choice of the software designed and deeply embedded in such systems to drive them for the purpose of realizing object detection and recognition [1], [2]. These impart the desired capability to the robotic vision systems for facilitating inspection, localization, registration, and manipulation of the objects for automation in different industries and extending the desired services in household applications. Object detection and recognition in the real environment has always presented formidable challenges to the engineering and scientific community since it first appeared on the horizon of the machine vision research [3]. Recognizing and detecting the objects of a particular class such as a human face, a car, a bird, an animal etc. in the static images make it even more challenging [4]. Notwithstanding the various complexities and diverse challenges associated with the domain of machine vision, the research in this domain has grown rapidly beyond these complexities especially in the last two decades since it has been both intensively and extensively researched across the scientific and engineering fraternities [5]. Object detection and recognition constitute an indispensable component of modern day intelligent systems which have shaped and cut across a broad spectrum of disciplines in contemporary human life such as security, health, defence, surveillance, medical diagnostics etc. where the issue of object detection and recognition need to be handled quickly and accurately. The research on object detection and recognition algorithms has spearheaded significant advancements in factory and office automation, assembly line industrial inspection systems as well as chip defect identification systems [6]. It has also resulted in appreciable and tangible progress in medical imaging, space exploration and biometrics. Different researchers over the years have approached the problem of object recognition and localization by developing methodologies based upon different techniques which are defined by the nature of the problem to be addressed. It can be easily observed that object detection and recognition are significantly influenced by the confounding parameters of pose, orientation, scale of object and environment parameters such as intensity, illumination [5]. The parameters of pose and orientation are observed to acquire more prominence when object detection and recognition are to be realized in environments which are characterized by occlusions, multiple objects in images, cluttering, overlapped objects in images and where the objects of a specific category undergo different orientations in the image plane [7-10]. It is also observed that the orientation parameter is given more attention than position and scale parameters when developing techniques centered around the O