Image Defect Detection Using Regional Attention Weighting
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Solution Overview
Problem
Existing image detection methods struggle to accurately identify defect images with slight or local defects due to limitations in feature extraction, leading to unreliable defect recognition.
Innovation Solution
An image processing method that performs feature extraction and defect detection on multiple regions of an image, adjusts prediction probabilities based on regional attention, and generates a final prediction result through adaptive integration, utilizing a trained recognition model with a feature extraction network, prediction network, and a classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If binary classification based on feature data recognition is used, then the detection process is simple, but the accuracy of recognizing defect images is low
Solution Approach 1:
The image is divided into multiple image regions, and feature extraction and defect detection are performed separately for each region. This segmentation allows the system to capture local defect characteristics that would be missed in global binary classification, thereby improving recognition accuracy while maintaining reasonable process complexity.
Solution Approach 2:
Different image regions are processed with attention mechanisms that assign different weights based on their importance. This local quality approach ensures that regions containing potential defects receive higher attention, improving the overall detection accuracy by focusing computational resources where they are most needed.
2Ease of manufacture
If feature extraction is performed on the entire image, then the process is straightforward, but local defects and slight defects cannot be reliably detected
Solution Approach 1:
The image is divided into multiple image regions, and feature extraction is performed separately for each region. This segmentation allows the system to capture local defect characteristics that would be missed in global feature extraction, thereby improving detection reliability for local and slight defects.
Solution Approach 2:
Instead of processing the entire image uniformly, the system performs partial processing on each image region separately. This partial action approach allows the system to focus computational effort on detecting local defects in each region, improving overall reliability without requiring excessive processing of the entire image at once.
3Measurement precision
If attention mechanism is added to adjust prediction probabilities, then the accuracy improves, but the computational complexity increases
Solution Approach 1:
The attention mechanism assigns different weights to different image regions based on their importance and defect likelihood. This local quality approach improves prediction accuracy by focusing computational resources on critical regions rather than treating all regions equally, making the increased computational complexity more efficient and justified.
Solution Approach 2:
The attention mechanism performs selective processing on image regions, allocating more computational effort to regions with higher attention scores and less to regions with lower scores. This partial action approach improves overall prediction accuracy while managing computational complexity by avoiding uniform high-cost processing across all regions.
Data Source
AI summary
An image processing method and apparatus that includes: performing feature extraction processing on N image regions of a to-be-processed image respectively to obtain feature data corresponding to the N image regions respectively, N being an integer greater than or equal to 1, performing defect detection on the N image regions respectively according to the feature data corresponding to the N image regions respectively to obtain a prediction probability of a defect in each of the N image regions, obtaining attention for each image region, adjusting the prediction probability of the defect in each image region according to the attention for each image region, and generating a prediction result of the to-be-processed image according to the adjusted prediction probability of each image region.