Object Detection via Region of Interest Segmentation and Confidence Thresholding
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Solution Overview
Problem
Computer digital image analysis remains computationally expensive and inaccurate, requiring large, power-consuming devices and often resulting in inaccurate object detection due to factors like poor lighting and image distortion.
Innovation Solution
The detection of some objects informs the sub-area of the digital image for subsequent analysis, limiting the image area and adjusting confidence thresholds based on relationships between objects, thereby enhancing computational and communicational efficiencies and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer digital image analysis is performed on the entire frame of the digital image to detect objects, then object detection coverage is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent divides the digital image into multiple regions of interest (ROIs) based on detected pivot points and associated objects. Instead of analyzing the entire image frame, the system segments and focuses computational resources only on relevant sub-regions where target objects are likely to appear, thereby reducing processing time while maintaining detection coverage.
Solution Approach 2:
The patent applies different analysis strategies to different regions of the image. High-priority regions containing pivot points and associated objects receive focused computational attention with adjusted confidence thresholds, while other regions are either analyzed with lower priority or excluded from analysis, optimizing the balance between detection accuracy and processing efficiency.
2Reliability
If computer digital image analysis is performed on the entire frame of the digital image to detect objects, then detection completeness is improved, but computational load increases
Solution Approach 1:
The patent extracts and isolates specific regions of interest from the full image frame based on pivot point detection. By taking out only the relevant sub-regions containing pivot points and associated objects for detailed analysis, the system reduces computational load while maintaining detection completeness for target objects.
Solution Approach 2:
The patent performs preliminary detection of pivot points and associated objects before conducting full object detection. This preliminary action identifies and marks regions that require detailed analysis, allowing the system to prepare and focus computational resources in advance, thereby reducing overall computational load while ensuring detection completeness.
3Stability of the object's composition
If uniform confidence thresholds are applied to all regions of the digital image, then detection consistency is maintained, but detection accuracy in specific contexts deteriorates
Solution Approach 1:
The patent applies different confidence thresholds to different regions of the image based on their contextual importance. Regions containing pivot points and associated objects use adjusted confidence thresholds that reflect their higher priority, allowing the system to maintain detection consistency across the image while improving detection accuracy in critical areas.
Solution Approach 2:
The patent dynamically adjusts confidence thresholds based on the detected context and region importance rather than using static uniform thresholds. This dynamic adjustment allows the system to adapt detection sensitivity to local conditions, improving accuracy in pivot point regions while maintaining overall detection consistency.
Data Source
AI summary
To improve the accuracy and efficiency of object detection through computer digital image analysis, the detection of some objects can inform the sub-portion of the digital image to which subsequent computer digital image analysis is directed to detect other objects. In such a manner object detection can be made more efficient by limiting the image area of a digital image that is analyzed. Such efficiencies can represent both computational efficiencies and communicational efficiencies arising due to the smaller quantity of digital image data that is analyzed. Additionally, the detection of some objects can render the detection of other objects more accurate by adjusting confidence thresholds based on the detection of those related objects. Relationships between objects can be utilized to inform both the image area on which subsequent object detection is performed and the confidence level of such subsequent object detection.