The proposed thesis presents a joint vision-based algorithmic scheme that makes industrial feeding systems, a major part of the modern automated manufacturing, more accurate and reliable. To overcome the problem of real-world industrial settings, including haphazard illumination, occlusions, and varying object properties, the research paper is best suited to integrate classic computer vision with the latest deep learning methods to capitalize on their benefits in object detection, orientation estimation, and picking points. The methodology is a two-step procedure: the synthesis of realistic scenes in the form of a rendering program gathers data, and authentic industrial images are used to verify the functionality of the system.