Candidate Object Identification for Worn Vehicle Components
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Solution Overview
Problem
Existing image recognition methods struggle to accurately identify specific components of vehicles when they are worn, broken, or dirty, as they often rely on pristine reference images, leading to difficulty in finding exact matches.
Innovation Solution
A control system that compares images of objects to be identified with a database of reference images depicting various conditions, including wear and breakage, using 3D models and machine learning algorithms to determine candidate objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image recognition uses pristine reference images, then the system is simple to operate, but it cannot accurately identify worn or damaged objects
Solution Approach 1:
The system pre-collects and stores reference images of objects in various conditions (new, worn, damaged) before actual identification needs arise. This preliminary preparation allows the system to handle diverse object states during operation without requiring complex real-time processing, thus improving identification accuracy while maintaining operational simplicity.
Solution Approach 2:
The system changes the parameter of reference image conditions from solely pristine states to multiple conditions including wear and damage levels. By incorporating objects at different life stages and states into the reference database, the system can accurately match objects regardless of their current condition, resolving the contradiction between identification accuracy and database complexity.
2Measurement precision
If the system compares objects to multiple reference conditions, then identification accuracy improves, but processing time increases
Solution Approach 1:
The reference image database is segmented into different categories or groups based on object conditions (e.g., new, worn, damaged). The system can then process reference images in a structured manner, comparing the input object against relevant segments only, which reduces unnecessary processing time while maintaining comprehensive accuracy across different object states.
Solution Approach 2:
The system employs partial action by selectively comparing reference images based on the apparent condition of the input object. When an object appears worn or damaged, the system prioritizes comparing it with reference images of similar conditions rather than exhaustively comparing with all possible reference images, thus reducing processing time while maintaining sufficient identification accuracy.
3Reliability
If the system uses traditional image recognition, then the device complexity is low, but it fails to identify exact matches for worn or damaged components
Solution Approach 1:
The system creates visual copies or representations of objects in various conditions within the reference database. By storing multiple copies of the same object type under different conditions (new, worn, damaged), the system can reliably match input objects to their correct references regardless of wear or damage, significantly improving identification reliability without requiring overly complex processing systems.
Solution Approach 2:
The system incorporates feedback mechanisms where the results of image comparisons are analyzed to improve future identifications. By learning from previous matching successes and failures, the system can refine its comparison algorithms and database structure, enhancing reliability for worn or damaged object identification while keeping the overall system complexity manageable through iterative improvement rather than initial over-engineering.
Data Source
AI summary
Aspects of the present invention relate to a control system for determining one or more candidate objects, an electronic device, and a method for determining one or more candidate objects. The control system comprises one or more processors collectively configured to: receive one or more images of an object to be identified; compare the one or more images of the object to a plurality of reference images corresponding to a plurality of known objects, the plurality of reference images comprising images of each of the known objects at a plurality of conditions; determine, in dependence on a similarity between the object to be identified and at least one reference image of at least one known object, one or more candidate objects from the plurality of known objects as potential matches with the object to be identified; and output an indication of the one or more candidate objects.