Region-of-Interest GAN Enhancement for Blurred Object Detection
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Solution Overview
Problem
Existing object detection systems in computer vision face challenges due to variations in camera lens production errors, leading to inconsistent pixel density and blur, which require extensive calibration and result in redundant work hours and error correction, especially when multiple objects need to be detected simultaneously.
Innovation Solution
Integrating a Generative Adversarial Network (GAN) as an additional layer in machine learning models like Mask-RCNN or SSD to enhance image regions of interest, improving object detection by generating high-quality images from blurred or low-resolution inputs, thereby enhancing pixel quality and detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Generative Adversarial Network is integrated to enhance image regions, then object detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image processing into distinct regions: regions of interest (ROIs) are extracted and enhanced separately from the rest of the image. The GAN is specifically applied only to these extracted ROIs rather than the entire image, allowing targeted enhancement while limiting computational overhead to only the necessary portions of the image.
Solution Approach 2:
The patent implements local quality by applying different processing qualities to different parts of the image. High-quality GAN-based enhancement is applied specifically to regions of interest where object detection is needed, while other areas receive standard processing. This ensures optimal quality where it matters most without uniformly increasing complexity across the entire system.
2Measurement precision
If GAN layer is added to enhance pixel quality, then detection accuracy improves, but computational resources increase
Solution Approach 1:
The patent extracts only the necessary regions of interest from the full image before applying the computationally intensive GAN enhancement. By taking out and isolating only the ROIs that contain potential objects of interest, the system avoids applying heavy computational resources to entire images, thereby reducing overall energy consumption while maintaining detection accuracy.
Solution Approach 2:
The patent applies partial action by using GAN enhancement only partially - specifically on extracted regions of interest rather than on the complete image. This partial application of the enhancement technique provides sufficient improvement for detection accuracy while avoiding the excessive computational resource consumption that would result from processing the entire image at high resolution.
3Manufacturing precision
If camera calibration is performed to correct lens errors, then pixel density consistency improves, but time consumption increases
Solution Approach 1:
The patent uses Generative Adversarial Networks to create enhanced copies of the extracted image regions. The GAN learns from training data to generate high-quality synthetic images that correct for lens distortions and pixel density variations, effectively creating corrected versions of the input regions without requiring time-consuming physical camera calibration procedures.
Solution Approach 2:
The patent applies parameter changes by using the GAN to transform the pixel-level parameters of the extracted regions. The neural network adjusts pixel density, sharpness, and other image parameters dynamically during processing, correcting calibration issues through learned transformations rather than through static pre-calibration procedures.
Data Source
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AI summary
The invention relates to a method (100) for enhancing an object detection, comprising the following steps: - Providing (101) an image, the image resulting from an acquisition by at least one sensor (3), - Extracting (102) at least one region of interest in the image by means of a machine learning model, - Applying (103) a Generative Adversarial Network to generate an enhanced image with an enhanced at least one region of interest based on the image and the extracted at least one region of interest, - Performing (104) an object detection in the enhanced image utilizing the enhanced at least one region of interest. Furthermore, the invention relates to a computer program, an apparatus, and a storage medium for this purpose.