Enhanced YOLOv8 Object Detection Model for Construction Worker Safety Using Image Transformations
Yash Seth, M. Sivagami
Abstract
The rapid growth of Deep Learning techniques plays a vital role in automation of manual work in various areas. One such area for application of new technology is that of Construction Worker Safety. It has thus become imperative to improve existing systems with the new capabilities of technology. This paper discusses a methodology of improving the performance of an existing approach of object detection, YOLOv8. The proposed work comprises of improved training of model and detection of helmet in worker images, using Test Time Augmentation (TTA) based approach. Image Transformations such as Histogram Equalization, Gamma Correction, Gaussian Blurring and Contrast Stretching are applied to augment the dataset by creating more versions of the existing data. This has shown to improve the performance of the model and also generalize better by preventing overfitting. A Test Time Augmentation-based Confidence Thresholding formula (TTACT) is also proposed, to improve the performance of helmet detection.