An Attempt at Automating Image Cropping Using YOLO

Abstract

Machine learning has been proposed for a wide range of applications, including materials development, medical diagnosis, and production and safety management, as it enables the extraction of useful patterns from complex data and facilitates prediction and optimization. To improve the performance of predictive and optimization tasks, enhancing the accuracy of machine learning models is essential. Enhancing model accuracy generally requires increasing the amount of training data. However, in many cases, the collected data cannot be used directly and must be converted into a format suitable for machine learning. In particular, image data often requires preprocessing such as cropping of target regions, which, when done manually, demands a significant amount of time and effort. In this presentation, we report on the implementation of automatic image cropping using YOLO-based object detection as an approach to address this issue.