Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer Architectures
Ikchul Eum, Jae-Jun Kim, Seunghyeon Wang, Ju-Hyung Kim
Abstract
Monitoring heavy equipment in real time is crucial for ensuring safety and operational efficiency at construction sites, yet achieving both high detection accuracy and fast inference remains challenging under diverse environmental conditions. Although previous studies have attempted to improve accuracy and speed, their findings often lack generalizability, partly due to inconsistent datasets and the need for more advanced techniques. In response, this study proposes an enhanced object detection method that integrates transformer-based backbone networks into the You Only Look Once (YOLO-version 10) framework. Evaluations conducted on a large-scale dataset of construction-site images demonstrate notable improvements in detecting the heavy equipment of varying sizes. Comparisons with other detectors confirm that the proposed model not only achieves higher accuracy but also maintains competitive processing speed, making it suitable for real-time deployment. Additionally, the dataset is made available for broader experimentation and development. These findings underscore the method’s potential to strengthen on-site safety by providing more reliable and efficient heavy equipment detection in complex work environments, while also acknowledging areas for further refinement.