Annotation Device Layout Optimization for Label Accuracy
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Solution Overview
Problem
In the construction of training data for machine learning, erroneous label additions during image annotation can occur due to operator mistakes, leading to suboptimal training data sets and reduced accuracy of recognition models.
Innovation Solution
An annotation device and method that utilize similarity information to determine the layout of labels on an operation screen, ensuring that similar labels are not adjacent, and an inspector to detect potentially erroneous labels by comparing recognition results with annotation data, thereby improving the detection of erroneous operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If labels are displayed in a simple sequential order on the operation screen, then the device complexity is reduced and ease of operation is improved, but the reliability of label selection decreases due to increased likelihood of erroneous operations between similar labels
Solution Approach 1:
The system performs preliminary analysis of label similarity relationships before displaying labels on the operation screen. By pre-calculating similarity metrics between labels and determining optimal layout arrangements in advance, the system prevents erroneous selections before they occur, rather than detecting and correcting them after annotation. This preliminary layout determination based on similarity information reduces selection errors while maintaining operational simplicity.
Solution Approach 2:
The patent introduces an intermediate layout determination step that acts as a mediator between the simple sequential display approach and the complex error prevention requirement. By inserting similarity-based layout arrangement as an intermediate layer, the system transforms the direct mapping from label list to screen display into an optimized arrangement that separates similar labels, thereby reducing erroneous operations without requiring complex real-time verification mechanisms.
2Area of stationary object
If similar labels are placed adjacent to each other on the operation screen for compact display, then the area of the operation screen is reduced, but the reliability of annotation decreases due to increased probability of erroneous label selection
Solution Approach 1:
The system applies asymmetric layout arrangement based on label similarity characteristics. Instead of uniform sequential placement or symmetric grouping, the layout is asymmetrically optimized to specifically separate similar labels while maintaining compact overall display. This asymmetric arrangement prioritizes preventing erroneous selections over minimizing screen area, strategically positioning dissimilar labels adjacent to each other and separating similar ones with greater spacing or intermediate elements.
3Measurement precision
If comprehensive inspection of all annotation data is performed to detect erroneous labels, then the measurement precision of error detection is improved, but the time required for annotation processing increases
Solution Approach 1:
The system performs preliminary error prevention through similarity-based layout arrangement, reducing the need for comprehensive post-annotation inspection. By preventing erroneous selections at the source through intelligent label positioning, the system reduces the burden on inspection mechanisms. The layout optimization acts as a preventive measure that reduces error rates before annotation occurs, thereby reducing both inspection time and maintaining detection accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the results of annotation inspections are used to refine and update label similarity information and layout arrangements for future annotation tasks. By feeding back inspection results into the system, it continuously improves the accuracy of similarity assessments and optimizes layouts to prevent recurring erroneous selections, thereby reducing inspection time over time while maintaining or improving detection precision.
Data Source
AI summary
An annotation device includes: a similarity information obtainer that obtains similarity information indicating whether or not a plurality of labels to be added as annotation data to images are similar to each other; a determiner that determines a layout of the plurality of labels to be displayed on an operation screen for an annotation operation based on the similarity information; a data obtainer communicator that obtains the annotation data added to the images using the operation screen; and an inspector that inspects the annotation data obtained by the second communicator for an erroneously added label.