Image Edge-Based CNN Architecture for Detecting Mosaic Augmentation in Datasets: A Novel Approach for Assuring Data Quality

This research uncovers a pioneering method for distinguishing mosaic augmentation in datasets, an area yet to be thoroughly investigated. Utilizing conventional visual preprocessing tactics, such as Gaussian blurring and Canny Edge Detection, together with a Convolutional Neural Network (CNN), we develop a strategy for detecting mosaic enhancements in datasets. This methodology achieved an remarkable accuracy rate. A custom-created dataset was used to verify our proposed model, which exhibited outstanding precision in classification tasks, therefore showcasing its capacity to assess whether a dataset has been subjected to mosaic augmentation. Our technique offers substantial enhancements to dataset validation procedures, enhancing the accuracy and trustworthiness of ensuing data-centric models or systems. The paper elucidates our methodological underpinnings, detailing the experimentation setup, and presents robust results validating our approach's efficacy. Specifically, it achieved an impressive precision of 94.84%, recall rate of 0.96, and an AUC of 0.98 on a proprietary dataset. These findings pledge potential for future applications in governing image data quality, persistently guaranteeing the genuineness and reliability of data across various AI and Machine Learning pursuits.