Multi-Modal LLM Object Detection From Environment Images
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Solution Overview
Problem
Images of environments are often stored without being utilized to determine information about objects within them, requiring manual assessment of object conditions and positions, which is inefficient and prone to errors.
Innovation Solution
Utilizing a trained multi-modal large language model to identify and locate objects within images, providing a record of their positions and conditions, and querying this record to automate monitoring and alerting for maintenance or repairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual assessment is used to determine object conditions and positions, then flexibility in handling diverse objects is maintained, but efficiency and accuracy deteriorate due to manual labor requirements
Solution Approach 1:
A multi-modal large language model serves as an intermediary between image data and monitoring decisions. The model processes images, identifies objects, determines positions and conditions, and generates structured outputs that can be queried to provide monitoring information, eliminating the need for complex custom processing systems for each object type
Solution Approach 2:
The multi-modal large language model provides universal functionality for monitoring diverse objects across different environments. A single model handles various object types (telecommunication equipment, street furniture, vegetation) and environments (indoor, outdoor, computing centers) through its general capabilities rather than requiring specialized systems for each object category
2Loss of information
If images are stored without automated processing, then storage simplicity is maintained, but loss of information occurs as object information is not extracted
Solution Approach 1:
The multi-modal large language model automatically extracts object information from images and generates structured records without requiring manual intervention. The system self-processes images, identifies objects, determines positions and conditions, and creates queryable records, transforming passive stored images into active information sources
Solution Approach 2:
Manual information extraction is replaced by automated multi-modal processing. The large language model uses its trained capabilities to analyze images and generate structured outputs, substituting the mechanical process of manual assessment with automated AI-based processing
3Loss of time
If manual monitoring is performed, then adaptability to unique object characteristics is maintained, but time consumption increases significantly
Solution Approach 1:
The system enables continuous monitoring by automatically processing images and maintaining updated records of object positions and conditions. The queryable records allow continuous retrieval of monitoring information without requiring repeated manual assessment, providing ongoing reliable information about object states
Data Source
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AI summary
A computer implemented method is defined. The method comprises receiving an image of an environment; providing the image to a trained multi-modal large language model trained to identify one or more objects within the image and a position of each of the one or more objects within the image and output text identifying the one or more objects and the positions of the one or more objects; receiving from the multi-modal large language model the output text; forming a record of the environment using the output text of the multi-modal large language model, wherein the record of the environment comprises an identification of the one or more objects in the environment and the position of the one or more objects in the environment; and querying the record to determine an identity and/or a position of an object of the one or more objects.