Vehicle Stowage Assistant Using Image-Based Placement Detection
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Solution Overview
Problem
Current systems lack an efficient method for determining optimal placement of objects within vehicles, relying on manual processes that are time-consuming and prone to errors, and do not effectively utilize advanced technologies like blockchain for secure and decentralized data management.
Innovation Solution
A system that uses image processing and machine learning to determine the bounding area of an object and identify suitable locations within a vehicle, integrated with blockchain technology for secure data management and authorization, enabling efficient object placement and secure data sharing among vehicles and service centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for determining object placement in vehicles, then system complexity is low, but productivity is reduced and errors increase
Solution Approach 1:
The patent replaces manual mechanical assessment of object placement with an image processing system that captures images of objects and vehicles, automatically determines bounding areas, and calculates optimal placement locations. This substitution of manual mechanical processes with automated visual analysis directly resolves the contradiction by dramatically improving productivity while managing system complexity through software-based solutions.
Solution Approach 2:
The patent creates digital copies (images) of objects and vehicles to analyze placement without physically manipulating the actual objects. By working with image data and bounding area representations rather than physical objects, the system achieves high productivity in determining placement locations without requiring complex physical handling mechanisms.
2Measurement precision
If image processing and machine learning are used to determine object placement, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex manual measurement processes with automated image processing algorithms that calculate bounding areas and determine placement locations with high precision. The machine learning components automatically learn optimal placement strategies from training data, achieving accurate measurements without requiring complex manual intervention systems.
Solution Approach 2:
The machine learning model performs self-learning and self-improvement through training on object and vehicle data. The system automatically adjusts its bounding area determination and placement recommendation algorithms based on training examples, achieving high measurement precision while managing complexity through automated self-optimization rather than requiring complex manual calibration systems.
3Reliability
If blockchain technology is integrated for secure data management, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent introduces blockchain technology as an intermediary layer for secure data management, authorization, and verification of object placement transactions. The blockchain acts as a trusted mediator that records and verifies data without requiring complex point-to-point security protocols between all system components, thereby improving reliability while managing architectural complexity through a standardized intermediary interface.
Data Source
AI summary
An example operation includes one or more of receiving an image of an object to place into a vehicle, determining a bounding area of the object based on the received image, determining a location in the vehicle to place the object based on the bounding area, and sending the determined location to be displayed.