A New Benchmark Model for the Automated Detection and Classification of a Wide Range of Heavy Construction Equipment

The integration of computer vision technology into construction sites poses various challenges due to the complex environment. Prior studies on computer vision related to heavy construction equipment has primarily focused on a limited range of equipment types provided in standard databases, such as the Microsoft Common Objects in Context (MS COCO) data set. The conventional approach has limitations in capturing the diverse working conditions and dynamic environments encountered in real construction sites. To overcome the challenge, this study proposes a new benchmark model for the automated detection and classification of a wide range of heavy construction equipment (i.e., nine representative types) commonly used in construction sites by using a deep convolution neural network. This study was conducted in four steps: (1) data collection and preparation, (2) data transformation, (3) model training, and (4) model validation. The proposed you only look once (YOLO)v5l (large, YOLOv5 with a larger network) model demonstrated high reliability, achieving a mean average precision (mAP)_0.5:0.95 of 90.26%. This study makes a significant contribution to the domain of construction engineering and management by providing a more efficient and systematic management system to proactively prevent heavy equipment-related safety accidents with diverse working conditions and dynamic environments encountered at construction sites. Moreover, the proposed approach can be extended to integrate advanced techniques such as case-based reasoning, digital twin, and blockchain, allowing for the automated activity recognition in various occlusions, the carbon emissions monitoring and diagnostics of heavy equipment, and a robust real-time construction management system with enhanced security.