Filtering-out poor-quality images for data preparation

Traditional image processing often relies on denoising techniques like Gaussian or median filters to improve image quality for tasks such as object detection. However, these methods often struggle with diverse environmental noise (e.g., rain, snow, darkness) and can inadvertently remove critical information or require image resizing that degrades performance. The authors propose a paradigm shift: rather than attempting to fix noisy images, they suggest filtering out poor-quality images entirely during the data preparation phase.

Their approach involves calculating a quality score for each image in a dataset using an image quality assessment metric. An optimal threshold is then determined to discard images that fall below a certain quality level, while ensuring that a sufficient number of images remain to train a robust deep learning model. This process is designed to prevent the model from learning from confusing or low-quality data.

The researchers evaluated their filtering approach using two datasets: the Challenging Unreal and Real Environments for Traffic Sign Recognition (CURE-TSR) and the Challenging Unreal and Real Environments for Object Recognition (CURE-OR). By removing poor-quality images, the proposed method achieved an average recognition accuracy of 93.8% for traffic signs and 84.9% for object recognition. These results significantly outperformed state-of-the-art denoising approaches, which typically achieved accuracies around 77% on the same datasets.

This study demonstrates that data quality is often more important than data quantity in deep learning applications. By prioritizing the selection of high-quality training samples, researchers can improve the performance of computer vision systems—such as those used in autonomous vehicles—without the computational overhead or potential information loss associated with complex denoising algorithms. This provides a practical, efficient, and highly effective strategy for preparing datasets for real-world deployment.