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Evaluating the Impact of Gaussian Noise on YOLO-Based Object Detection: A Systematic Study on Noise Robustness and Denoising-Retraining Strategies

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By Rene Jaros, Vo Thanh Kiet, Boris Pustejovsky
| preprints.org
You are already at the latest version Object detection systems based on the You Only Look Once (YOLO) family have achieved strong performance under controlled conditions, but their reliability often deteriorates when input images are corrupted by sensor noise, low-light artifacts, or transmission errors. This study evaluates the impact of Gaussian noise on YOLO-based object detection and examines whether image denoising combined with model retraining can recover the lost robustness.
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