A posture detection method for augmented reality–aided assembly based on YOLO-6D

Abstract

The assembly of small batch electrical and mechanical products still relies on manual operation, with the problems of high rechecking time and low assembly efficiency and quality. The augmented reality technology can be used to assist assembly to improve efficiency. In view of the high professionalism, labor and time cost for the traditional posture detection method, a posture detection method is proposed to match the assembly posture by pre-calibrating the assembly posture template. YOLO-6D deep learning network is brought in to increase the accuracy of virtual information tracking registration. And the detection from both translation and rotation perspectives is designed to enhance the adaptability to different assembly tasks. For the generation of training datasets, a weighted sampling method based on part features is proposed to optimize the accuracy of position estimation with the limited training samples. Taking the assembly process of typical electronic products as an example, the developed augmented reality aided assembly system is used to boost the assembly efficiency of assembly operators. It is illustrated that the proposed positional detection method is effective in registering virtual assembly guidance information in real scenarios and can be applied to augmented reality assembly guidance of products.

Augmented Reality Based Distant Maintenance Approach

“…Such advancements would enhance the efficiency and effectiveness of the remote Finally, test the AR application on a device that has the Vuforia engine installed. To test the application, build it for the device and then deploy it [21]. The AR content should appear on the image target in real time when viewed through the device camera.…”