Image Retrieval Module Using CNN Feature Vectors for Inspection
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Solution Overview
Problem
Existing inspection systems face inefficiencies in analyzing component images due to the need to process and filter large databases of image files, often retrieving irrelevant information and increasing inspection time.
Innovation Solution
The implementation of an image retrieval module using a convolutional neural network that determines feature vectors and hash codes to filter and sort relevant image files, focusing on specific regions of interest and utilizing self-learning to enhance accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the inspection system processes each image file in the database to find relevant image files, then the user can access image files for inspection, but the time required to inspect the components is increased
Solution Approach 1:
The system performs preliminary processing of image files by extracting feature vectors and generating hash codes during database population, rather than processing images in real-time during inspection. This pre-computation stores essential image characteristics in an optimized format that enables rapid retrieval during actual inspection operations, resolving the contradiction between retrieval accuracy and inspection time.
Solution Approach 2:
The system introduces hash codes as an intermediary representation between raw image files and retrieval operations. By converting images to compact hash code representations based on extracted feature vectors, the system enables rapid comparison and filtering without processing full-resolution images, thus maintaining retrieval accuracy while dramatically reducing inspection time.
2Loss of information
If the inspection system retrieves image files from the database, then relevant information can be provided, but irrelevant image files may be retrieved that obscure useful information
Solution Approach 1:
The system extracts feature vectors that capture local characteristics and regions of interest within images, rather than treating entire images as uniform units. By comparing specific feature vectors and hash codes derived from salient regions, the system retrieves images with locally relevant characteristics while filtering out irrelevant portions, improving both information relevance and inspection efficiency.
Solution Approach 2:
The system transforms images from their original complex visual form into simplified parameter representations (feature vectors and hash codes). This parameter transformation enables efficient filtering and comparison operations that identify relevant images based on key characteristics, eliminating irrelevant results and improving both retrieval accuracy and productivity.
3Measurement precision
If a database of image files is used to assist users in inspecting components, then analysis accuracy can be improved, but the system must process and filter large amounts of data increasing complexity
Solution Approach 1:
The system extracts essential features from images and stores them as separate feature vectors and hash codes in the database. This extraction separates the critical analytical information from the full image data, allowing the system to maintain high measurement precision through feature comparison while reducing data processing complexity by working with compact numerical representations rather than large image files.
Data Source
AI summary
A method of inspecting a component using an image retrieval module includes storing an inspection image file in a memory and identifying a region of interest in the inspection image file. The method further includes accessing a database storing image files and determining feature vectors associated with the image files. The method also includes determining a hash code for each image file based on the feature vectors and classifying a subset of image files as relevant based on the hash codes. The method further includes sorting the subset of image files based on the feature vectors and generating search results based on the sorted subset of image files. The image retrieval module includes a convolutional neural network configured to learn from the determination of the feature vectors and increase the accuracy of the image retrieval module in classifying the image files.