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Updated: Oct 14, 2025

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It GAN Do Better: GAN-Based Detection of Objects on Images With Varying Quality
This study introduces a new framework using Generative Adversarial Networks (GANs) to improve object detection in low-quality images. The GAN-DO framework enhances robustness and accuracy without increasing model complexity.
Area of Science:
- Computer Vision
- Machine Learning
- Deep Learning
Background:
- Object detection models often struggle with reduced image quality, impacting performance.
- Image quality degradation affects both classification and bounding box regression accuracy.
- Existing methods lack robustness for real-world, variable image quality scenarios.
Purpose of the Study:
- To propose a novel Generative Adversarial Network (GAN)-based framework, GAN-DO, for robust object detection.
- To enhance deep neural network (DNN) architectures for improved performance on low-quality images.
- To evaluate the impact of image quality on object detection tasks.
Main Methods:
- Developed a GAN-based Detection of Objects (GAN-DO) framework adaptable to various DNN architectures.
- Generated features using GANs to improve robustness against reduced image quality.
- Evaluated the framework's performance on object detection datasets using state-of-the-art baseline models.
Main Results:
- The GAN-DO framework demonstrated improved robustness to varying image quality.
- Achieved higher mean Average Precision (mAP) compared to existing approaches.
- The framework maintained baseline model architecture, complexity, and inference speed.
Conclusions:
- The proposed GAN-DO framework effectively enhances object detection robustness for reduced-quality images.
- GAN-DO offers a generalizable solution applicable to multiple DNN architectures.
- The method provides a significant improvement in object detection accuracy under challenging image conditions.
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