Infrared Image Augmentation for Low-Contrast Object Detection
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Solution Overview
Problem
Infrared images lack color information and have poor contrast between objects and backgrounds, making it difficult to recognize objects, especially in rainy weather, and are prone to noise and blurred edges, which hinders effective object detection.
Innovation Solution
An image processing device that creates augmented images based on infrared detection signals, using machine learning models, by imparting specific contrasts to target areas and generating multiple images with varying contrasts to enhance object detection performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If infrared detection is used to capture thermal images, then object detection can be performed in low light or dark conditions, but the images suffer from poor contrast between objects and backgrounds, making it difficult to recognize objects
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate images with different contrast adjustments before final object detection. The contrast adjustment unit creates several versions of the infrared image with varying contrast levels, and the selection unit chooses the most appropriate one for detection, thereby resolving the contrast information loss problem before detection occurs
Solution Approach 2:
The invention changes the contrast parameter of infrared images by generating multiple candidate images with different contrast adjustments. The contrast adjustment unit modifies the contrast parameter to create variations, allowing the system to select an optimal contrast level that enhances object-background differentiation while preserving detection capability in low light conditions
2Manufacturing precision
If standard normalization is applied to infrared detection signals, then the full dynamic range is utilized, but edges blur and noise increases especially in rainy weather
Solution Approach 1:
Instead of applying uniform normalization across the entire image, the system applies contrast adjustment selectively to specific regions or applies multiple levels of contrast adjustment to generate candidate images. This partial action approach allows some areas to maintain original characteristics while others receive enhanced contrast, reducing edge blurring and noise amplification
Solution Approach 2:
The system performs preliminary contrast adjustment to generate multiple candidate images before final detection. By pre-processing the images with different contrast levels, the system prepares multiple options that can withstand adverse weather conditions, selecting the most reliable one for object detection
3Area of stationary object
If the entire screen is processed for object detection, then comprehensive coverage is achieved, but processing time increases and specific targets are harder to identify
Solution Approach 1:
The invention segments the infrared image into multiple candidate images with different contrast adjustments, allowing the system to evaluate multiple regional variations simultaneously. This segmentation approach enables focused attention on specific areas with appropriate contrast levels, reducing the effective processing area while maintaining comprehensive coverage through multiple candidates
Solution Approach 2:
The system performs preliminary generation of multiple candidate images with different contrast adjustments before the actual detection process. This pre-processing step organizes the data into manageable candidates, allowing subsequent detection to focus on selecting and processing only the most relevant candidate images rather than the entire original image set
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the performance of detecting objects in infrared images by emphasizing target areas and reducing noise, enhancing the robustness and accuracy of machine learning models.
Implementation Method 1
a camera using a sensor (this is referred to as a sensor camera). The infrared image generator generates an infrared image by converting infrared rays emitted from an object into an electrical signal
Data Source
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AI summary
An image processing device (100) is provided with an image augmentation unit (40) that creates, from an infrared detection signal obtained by imaging an object and on the basis of information about the object, expansion images (71-78) for use as training data for machine learning model creation, or for use as input images in object recognition processing by using a machine learning model.