Automated Dataset Amplification and its Application to Small Dataset Object Detection Transfer Learning

Abstract

∗Object detection is a core process for many image processing applications. Using the YoloV3 deep learning approach to object detection, which is trained on a fixed set of objects, transfer learning is applied to learn the features of novel construction objects. Transfer learning typically requires a large dataset of both images and labels, and labeling image data can take a long time. This paper will introduce several preprocessing pipeline approaches as a means of data amplification and data augmentation to enhance a small dataset using a combination of the following transformations: rotation, scaling, flipping, and grayscale conversion. A construction safety helmet detection model is trained using various experimental data preprocessing pipelines and the results are presented.