Object Region Importance Generation for Image Recognition Data
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Solution Overview
Problem
Generating high-accuracy learning data for image recognition models is challenging due to the need for large datasets, and existing methods are inefficient in preparing data that effectively accounts for object orientation and image quality.
Innovation Solution
A data generation apparatus and method that acquires multiple images of an object from different angles, cuts out object regions, generates importance information based on factors like sharpness and size, and stores this data to reduce the impact of low-quality images on the model, facilitating the creation of accurate object inference models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of learning data are prepared to improve model accuracy, then the accuracy of the generated model is improved, but the time and resources required for data preparation increase
Solution Approach 1:
The patent segments the image data by cutting out object regions from multiple images and generating importance information for each region. This segmentation allows the system to process and evaluate individual object regions separately, enabling efficient selection of high-quality training samples without manually reviewing entire images, thus reducing data preparation time while maintaining model accuracy.
Solution Approach 2:
The patent performs preliminary processing by generating importance information that indicates the quality and suitability of object regions before actual model training. This preliminary action identifies and prioritizes high-quality regions in advance, allowing the system to efficiently select appropriate training data without time-consuming manual evaluation during the data preparation phase.
2Measurement precision
If multiple images are processed to improve learning data quality, then the learning effect is improved, but the processing complexity increases
Solution Approach 1:
The patent implements self-service by generating importance information automatically through computational processing of object regions. The system autonomously evaluates image quality metrics and generates importance scores without requiring manual intervention or complex external processing systems, thereby improving learning data quality while keeping processing complexity manageable.
3Productivity
If importance information is generated for each object region to improve data selection efficiency, then the learning effectiveness is improved, but the computational processing required increases
Solution Approach 1:
The patent applies local quality by generating importance information specifically for object regions rather than processing entire images uniformly. This localized approach concentrates computational resources on relevant areas, improving data selection efficiency by identifying high-quality training regions without the excessive computational cost of processing complete images at full resolution.
Data Source
AI summary
An image acquisition unit 110 acquires a plurality of images. The plurality of images include an object to be inferred. An image cut-out unit 120 cuts out an object region including the object from each of the plurality of images acquired by the image acquisition unit 110. An importance generation unit 130 generates importance information by processing the object region cut out by the image cut-out unit 120. The importance information indicates the importance of the object region when an object inference model is generated, and is generated for each object region, that is, for each image acquired by the image acquisition unit 110. A learning data generation unit 140 stores a plurality of object regions cut out by the image cut-out unit 120 and a plurality of pieces of importance information generated by the importance generation unit 130 in a learning data storage unit 150 as at least a part of the learning data.