Better AI starts with better data: Researchers identify hidden annotation errors in object detection datasets
Researchers from Sejong University have published a survey identifying significant annotation errors in object detection datasets used for AI training. The study emphasizes the need for data-centric quality control to improve the reliability of computer vision models.
Why it matters
As AI systems become critical in fields like autonomous driving and medicine, the integrity of training data is essential for safety and performance.
edited by Sadie Harley , reviewed by Robert Egan
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Add as preferred source An illustration of sample images from each dataset, highlighting their diversity. Credit: Artificial Intelligence Review (2026). DOI: 10.1007/s10462-026-11502-z A research team led by Sung Wook Baik, a professor in the Department of Software at Sejong University, has published a comprehensive survey examining annotation errors in widely used object detection datasets and methods for identifying and validating them.
The paper, titled " Quality over quantity: a data-centric survey of annotation errors in object detection datasets ," was published in Artificial Intelligence Review . The study takes a data-centric approach to object detection by focusing on the quality and reliability of the datasets used to train and evaluate computer vision models.
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