Development of semi-automatic image annotation using object recognition
Hiroki Tanioka, Tsuyoshi Miura, Kenji Matsuura, Stephen Karungaru · 2023
The modern field of machine learning has made remarkable progress, and the demand for training data required by machine learning systems is constantly increasing. However, the annotation work required to create this training data requires a great deal of effort. Therefore, we considered semi-automating the labeling process by taking advantage of false positives that occur when object detection is performed by YOLOv5, thereby eliminating the need for region selection and reducing the cost of annotation work. In a comparison experiment with labelImg, a common annotation tool, we found that semi-automating the annotation process took less time than manually annotating the objects.