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As a result of an increasingly automatized and digitized industry, processesare becoming more complex. Augmented Reality has shown considerable potentialin assisting workers with complex tasks by enhancing user understanding andexperience with spatial information. However, the acceptance and integration ofAR into industrial processes is still limited due to the lack of establishedmethods and tedious integration efforts. Meanwhile, deep neural networks haveachieved remarkable results in computer vision tasks and bear great prospectsto enrich Augmented Reality applications . In this paper, we propose anAugmented-Reality-based human assistance system to assist workers in complexmanual tasks where we incorporate deep neural networks for computer visiontasks. More specifically, we combine Augmented Reality with object and actiondetectors to make workflows more intuitive and flexible. To evaluate our systemin terms of user acceptance and efficiency, we conducted several user studies.We found a significant reduction in time to task completion in untrainedworkers and a decrease in error rate. Furthermore, we investigated the userslearning curve with our assistance system.

Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks