Learning Apparatus Background Object Labeling for Detection Accuracy
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Solution Overview
Problem
Existing object detection techniques struggle to improve accuracy by considering background objects within the extracted region, leading to potential misclassification of target objects.
Innovation Solution
A learning apparatus and method that includes an acquisition unit for image and label data, an object candidate extraction unit, a prediction unit, a correct answer data generation unit for background object labels, and an optimization unit to refine object candidate extraction and prediction using the generated correct answer data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If object detection focuses only on target objects in extracted regions, then detection speed is improved, but detection accuracy deteriorates due to ignoring background objects
Solution Approach 1:
The detection process is segmented into two distinct stages: first extracting object candidate rectangles from the image, then generating background object labels for each candidate rectangle. This segmentation allows the system to efficiently process target objects while separately considering background objects, thus maintaining both speed and accuracy
Solution Approach 2:
Background object labels are generated in advance as correct answer data before the final detection decision is made. By pre-processing and labeling background objects within candidate rectangles, the system prepares detection data ahead of time, enabling faster and more accurate detection without compromising speed
2Measurement precision
If background objects are considered in extracted regions, then detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The object candidate extraction unit serves multiple functions: it extracts not only target object candidates but also identifies regions containing background objects. This multi-functionality allows the same extraction mechanism to support both target detection and background object labeling, reducing overall system complexity while improving accuracy
3Measurement precision
If background objects are considered in extracted regions, then detection accuracy is improved, but computational cost increases
Solution Approach 1:
The system generates background object labels only for regions within object candidate rectangles, not for the entire image. This partial action approach focuses computational resources only where needed - within candidate regions - thereby reducing overall computational cost while still improving detection accuracy through background object consideration
Data Source
AI summary
In a learning apparatus, an acquisition unit acquires image data and label data corresponding to the image data. An object candidate extraction unit extracts each object candidate rectangle from the image data. A correct answer data generation unit generates a background object label corresponding to each background object included in each object candidate rectangle as correct answer data corresponding to the object candidate rectangle by using the label data. A prediction unit predicts a classification using each object candidate rectangle and outputs a prediction result. An optimization unit optimizes the object candidate extraction unit and the prediction unit using the prediction result and the correct answer data.