Visual Inspection Classifier Using Logical Operator Fusion
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Solution Overview
Problem
Complex visual inspection tasks in manufacturing and logistics are typically performed manually, lacking automation and consistency, and existing automated systems are inefficient in combining multiple simple inspection tasks to make comprehensive decisions.
Innovation Solution
A system and method for automatic visual inspection using a combination of feature extractors, such as convolutional neural networks, and simple classifiers, along with logical operators to classify objects based on visual information, enabling the automation of complex inspection tasks by capturing and processing 2D and 3D data from multiple cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual inspection is used for complex visual inspection tasks, then flexibility and adaptability are maintained, but productivity and consistency deteriorate
Solution Approach 1:
The complex inspection task is segmented into multiple simple inspection tasks, each handled by a dedicated simple classifier. This allows the system to maintain adaptability for different inspection scenarios while achieving high productivity through automated parallel processing of multiple classification tasks.
Solution Approach 2:
The complex classifier is designed as a universal system that can handle multiple types of inspection tasks simultaneously by combining multiple simple classifiers. This multi-functional approach enables the system to adapt to various inspection requirements while maintaining high automated productivity.
2Reliability
If manual inspection is used for complex visual inspection tasks, then comprehensive evaluation can be performed, but reliability and consistency deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the complex classifier receives inputs from multiple simple classifiers and combines their outputs using logical operators. This feedback loop ensures consistent and reliable decision-making by aggregating multiple classification results, while maintaining high automated efficiency.
Solution Approach 2:
Multiple simple classifiers are merged into a complex classifier that combines their outputs using logical operators. This merging approach enhances reliability and consistency by integrating multiple evaluation perspectives, while achieving high productivity through automated parallel processing.
3Productivity
If existing automated systems are used for complex inspection tasks, then productivity is improved, but the ability to combine multiple simple inspection tasks effectively deteriorates
Solution Approach 1:
The system segments the complex inspection task into multiple simple inspection tasks, each handled by a dedicated simple classifier. This segmentation simplifies the overall system design by breaking down complexity into manageable, independent modules that can be easily combined using logical operators.
Solution Approach 2:
The system transitions from handling single inspection tasks to multiple tasks by adding a dimensional layer of complexity through the complex classifier. This dimensional change allows effective combination of multiple simple classifiers while maintaining productivity through automated processing.
4Measurement precision
If multiple simple inspection tasks are combined manually, then comprehensive decisions can be made, but loss of time and efficiency deteriorate
Solution Approach 1:
The system maintains continuous automated processing by having multiple simple classifiers operate in parallel and have their outputs continuously combined by the complex classifier. This continuous action ensures comprehensive inspection coverage without time loss, as all classification tasks are processed simultaneously rather than sequentially.
Solution Approach 2:
Multiple simple classifiers are merged into a unified complex classifier system that processes multiple inspection tasks simultaneously. This merging enables comprehensive inspection decisions to be made in parallel, eliminating time loss associated with sequential manual evaluation.
Data Source
AI summary
A method for performing automatic visual inspection includes: capturing visual information of an object using a scanning system including a plurality of cameras; extracting, by a computing system including a processor and memory, one or more feature maps from the visual information using one or more feature extractors; classifying, by the computing system, the object by supplying the one or more feature maps to a complex classifier to compute a classification of the object, the complex classifier including: a plurality of simple classifiers, each simple classifier of the plurality of simple classifiers being configured to compute outputs representing a characteristic of the object; and one or more logical operators configured to combine the outputs of the simple classifiers to compute the classification of the object; and outputting, by the computing system, the classification of the object as a result of the automatic visual inspection.