Image Focus Scoring for Consistent Object Detection Capture
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Solution Overview
Problem
Existing image detection systems rely on subjective human evaluation and complex objective evaluation methods, which are prone to human error and require precise positioning of objects, leading to inconsistent evaluation standards and inefficient image capture.
Innovation Solution
An image detection auxiliary system utilizing a deep learning-based classification model to automatically detect and crop objects, calculate an image focus score, and provide a unified evaluation standard, ensuring objects are in focus before capture and allowing for real-time adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If subjective human evaluation is used to determine image focus, then flexibility in assessment is improved, but evaluation consistency deteriorates due to human factors
Solution Approach 1:
The patent replaces the mechanical human visual evaluation system with an automated image processing system that uses objective algorithms (edge detection, gradient analysis, frequency domain methods) to calculate focus scores. This substitution eliminates human subjectivity while maintaining evaluation flexibility through configurable parameters and multiple assessment methodologies.
Solution Approach 2:
The patent transforms the qualitative subjective evaluation into quantitative parameters by calculating specific focus metrics such as edge sharpness, gradient magnitude, and frequency content. These measurable parameters enable consistent automated assessment while allowing flexibility in selecting which parameters to prioritize based on application requirements.
2Measurement precision
If complex objective evaluation algorithms are used, then evaluation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the image evaluation process into distinct modular stages: preprocessing (noise reduction, contrast enhancement), feature extraction (edge detection, gradient calculation, frequency analysis), and scoring (focus metric computation, threshold comparison). This segmentation allows each module to be optimized independently and simplifies the overall complex process into manageable components.
Solution Approach 2:
The patent implements multiple evaluation algorithms and metrics but allows selective application based on computational resources and requirements. Not all complex algorithms need to run simultaneously; the system can apply simpler methods for quick assessments and more complex methods when higher accuracy is needed, balancing accuracy with processing complexity.
3Adaptability or versatility
If manual image capture with repositioning is used, then capture flexibility is improved, but capture efficiency deteriorates due to repeated adjustments
Solution Approach 1:
The patent implements real-time feedback by continuously analyzing captured images for focus quality and providing immediate evaluation results. This feedback loop allows operators to see focus assessment outcomes instantly and make rapid adjustments without manual repositioning, significantly improving capture efficiency while maintaining flexibility through interactive control.
Solution Approach 2:
The patent performs preliminary focus evaluation on captured images before final acceptance or rejection. By pre-assessing focus quality using automated algorithms, the system identifies acceptable images early in the workflow, eliminating the need for manual repositioning and repeated captures, thus improving efficiency while preserving capture flexibility.
Data Source
AI summary
Disclosed are an image detection auxiliary system and an image detection method. An image capture device is used to generate an original image. A computing device receives the original image and preprocesses the original image as an input image. The computing device uses a classification model to obtain a feature parameter corresponding to the input image, and inputs the feature parameter to a probability function to calculate an image focus score. Finally, the computing device performs formal detection or subsequent processing of generating a second warning signal according to the image focus score.