AR Assembly Guidance Using Real-Time Object Detection
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Solution Overview
Problem
Current augmented reality solutions fail to provide a useful and convenient user experience for both consumers and product providers in assisting with object identification and product assembly, as they lack effective real-time guidance and automation.
Innovation Solution
An augmented reality assembly guidance system utilizing real-time object detection through artificial intelligence and deep learning computer vision models, which generates AR guidance by labeling parts and providing visual cues such as labels, arrows, and multimedia instructions, allowing users to assemble products autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current AR solutions are used for product assembly guidance, then digital content can be overlaid on camera feed, but real-time object identification and automated guidance are not achieved
Solution Approach 1:
The system enables self-service by allowing the AR guidance system to automatically identify parts and provide assembly instructions without requiring manual input from the user. The computer vision system autonomously detects objects and overlays relevant guidance content, making the system serve itself in terms of information retrieval and presentation.
Solution Approach 2:
The patent replaces manual mechanical operations with automated computer vision and AI systems. Instead of users manually searching for parts or following physical manuals, the system uses machine learning models to automatically identify components and provide digital overlays, substituting human cognitive and physical effort with automated computational processes.
2Productivity
If manual part identification and assembly guidance is used, then users can follow instructions, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-identifying parts and assembly instructions before the user needs them. The computer vision system continuously analyzes the camera feed and prepares guidance content in advance, so that when a user points the camera at a part, the identification and relevant instructions are already ready for immediate display, eliminating search and wait time.
Solution Approach 2:
The patent maintains continuity of useful action by implementing real-time, continuous object detection and guidance overlay. The system processes video frames continuously, maintaining constant awareness of the workspace and providing uninterrupted guidance, rather than requiring periodic manual checks or interruptions in the assembly flow.
3Measurement precision
If simple AR overlay is used, then digital content can be displayed, but accurate real-time object detection and labeling are not achieved
Solution Approach 1:
The system achieves multi-functionality by integrating computer vision, object detection, part identification, and AR overlay capabilities into a single unified platform. The same system handles multiple tasks including real-time video processing, machine learning inference, part recognition, and graphical overlay generation, reducing the need for separate specialized devices while maintaining high accuracy.
Solution Approach 2:
The patent uses an intermediary approach by introducing trained machine learning models as mediators between the raw camera input and the final AR guidance output. These models act as intelligent intermediaries that process visual data, identify patterns, and translate them into meaningful part classifications and assembly instructions, bridging the gap between simple image capture and complex guidance generation.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for utilizing non-RAM memory to implement a cache. An embodiment operates by providing a graphical user interface for entering information associated with assembly instructions and receiving assembly instruction information through the user interface. The assembly instruction information include a computer vision real-time object detection model trained to identify a plurality of parts or objects in a video stream, one or more instruction step nodes a mapping to at least one of the plurality of parts or objects identifiable by the computer vision real-time object detection model, and one or more attributes associated with the one or more instruction step nodes. An assembly instruction set is generated based on the received assembly instruction information and transmitted to a client device for displaying a graphical user interface showing graphical elements superimposed on a video stream based on the assembly instruction set.