Image Processing Method for Object Detection Quality Evaluation
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Solution Overview
Problem
Existing image processing methods face challenges in accurately recognizing objects from quality-degraded images due to factors like blurriness, low illumination, and steep angles, leading to increased processing time and erroneous matching results.
Innovation Solution
A processor-implemented image processing method that evaluates the quality of an image using an AdaBoost algorithm, calculates an evaluation score, and decides whether to perform object detection based on this score, suspending detection for low-quality images to enhance accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If typical image processing approach is used to recognize objects from quality-degraded images, then object detection can be performed, but recognition accuracy deteriorates and processing time increases
Solution Approach 1:
The patent applies preliminary action by performing image quality evaluation before object detection. The system calculates quality scores for multiple images in advance, identifies high-quality images, and then performs object detection only on those selected images. This preliminary filtering action prevents wasting processing time on low-quality images that would yield poor recognition results, thereby resolving the contradiction between maintaining accuracy and reducing processing time.
2Measurement precision
If typical image processing approach processes all images, then comprehensive object detection is attempted, but erroneous matching results increase due to low recognition accuracy
Solution Approach 1:
The patent applies local quality principle by evaluating and selecting images based on their individual quality characteristics. Instead of uniformly processing all images, the system calculates quality scores for each image and selectively processes only those meeting a threshold. This localized quality-based filtering ensures high detection accuracy for processed images while maintaining overall processing throughput by excluding unsuitable images.
3Reliability
If object detection is performed on all images including low-quality ones, then detection coverage is maximized, but false positives increase due to image degradation
Solution Approach 1:
The patent applies preliminary action by implementing a quality evaluation step before object detection. The system calculates quality scores for images, compares them against thresholds, and only proceeds with detection on high-quality images. This preliminary filtering action reduces false positives caused by image degradation while maintaining manageable processing complexity through systematic quality-based selection.
Data Source
AI summary
An image processing method implemented by a processor includes receiving an image, acquiring a target image that includes an object from the image, calculating an evaluation score by evaluating a quality of the target image, and detecting the object from the target image based on the evaluation score.