GAN-based Image Data Augmentation for Improving Object Detection Performance in Industrial Environments

Object detection is one of the important industrial safety technologies that can automatically provide a worker with alerts to avoid unexpected near misses.However, deep learning-based object detection models require large amounts of training data to achieve higher performance, and data collection and labeling work is laborious and requires human resources.To address these limitations, we propose a GAN-based data augmentation that can supplement the original dataset with more diverse examples.In addition, we present a transformer-based generator network to improve the fidelity of generated data and evaluate the existing object detection model(YOLOv5) trained under different augmentation settings for a comparison study.The evaluation results show that the classification ability of the model trained with 20% augmented data has improved by 0.9% without localization performance losses.