Design and Development of Screw Detection System : A case study - Shruti Sabale; Shraddha Bhawar; Dinesh Gujar; Payal Rajput; S.P. Palekar (International Journal of Advanced Robotics and Automation Technology, 2026)
This study explores the design of a vision-based screw detection and orientation system for industrial automation, inspection, and robot disassembly. By integrating machine learning algorithms like region-based convolutional neural networks (R-CNN) with traditional image processing and impedance sensing, the system performs real-time screw presence detection, head type identification, and alignment. Three key technologies—deep learning classification, edge-based geometric analysis, and impedance verification—are integrated into a single modular system. The findings indicate that the proposed hybrid system greatly improves detection accuracy, automates disassembly, and complies with Industry 4.0 demands. In addition to detection and orientation, the system architecture is designed with scalability and adaptability in mind, enabling seamless deployment across diverse manufacturing environments. A high-resolution industrial camera captures continuous image streams, which are pre-processed using noise reduction, contrast enhancement, and normalization techniques to ensure consistent performance under varying lighting conditions. The extracted image features are then processed through the R-CNN framework to localize screws within complex assemblies, even in cluttered or partially occluded scenarios. To enhance robustness, the system incorporates edge-based geometric analysis for precise contour extraction and slot orientation measurement. This approach enables accurate differentiation between various screw head types such as Phillips, flathead, Torx, and hexagonal configurations. The integration of impedance sensing further validates screw engagement status during robotic manipulation, ensuring that the detected screw is physically accessible and correctly aligned before initiating disassembly. This multimodal verification significantly reduces false positives and operational errors. The modular design allows independent calibration of each subsystem—vision processing, classification, geometric analysis, and impedance sensing—thereby facilitating easier maintenance and upgrades. Real-time feedback mechanisms are implemented to support adaptive robotic control, where orientation data is transmitted directly to robotic end-effectors for precise torque application and unscrewing operations.
Keywords: Machine Learning; R-CNN; Screw detection; automated disassembly; hough transform; impedance sensor; robotic vision; smart manufacturing