Development of a faster classification system for metal parts using machine vision under different lighting environments

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A machine vision system for the automatic classification process is developed under different lighting environments, and has been applied to the operation of a robot arm with 6 degrees of freedom (DOF). In order to obtain accurate positioning information, the overall image is captured by a CMOS camera which is mounted above the working platform. The effects of back-lighting and front-lighting environments to the proposed system were investigated. With the front-lighting environment, four different conditions were performed. For each condition, global and local contrast threshold operations were used to obtain good image quality. In this study, a quadratic transformation used to describe the relationship between the image coordinates and the world coordinates was proposed, which has been compared to linear transformation as well as the camera calibration model in MATLAB tool. Experimental results show that in a back-lighting environment, the image quality is improved, such that the positions of the centers of objects are more accurate than in a front-lighting environment. According to the calibration results, the quadratic transformation is more accurate than other methods. By calculating the calibration deviation using the quadratic transformation, the maximum positive deviation is 0.48 mm and 0.38 mm in the X and Y directions, respectively. The maximum negative deviation is − 0.34 mm and − 0.43 mm in X and Y directions, respectively. The proposed system is effective, robust, and can be valuable to industry, as it offers an automated robotic system with an improved flexibility for separating dissimilar items of nominal value, in order to reclaim material investments, and decrease the expense of purchasing the same items again.