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With the increasing complexity of construction site environments, robust object detection and segmentation technologies are essential for enhancing intelligent monitoring and ensuring safety. This study investigates the application of YOLOv11-Seg, an advanced target segmentation technology, for intelligent recognition on construction sites. The research focuses on improving the detection and segmentation of 13 object categories, including excavators, bulldozers, cranes, workers, and other equipment. The methodology involves preparing a high-quality dataset through cleaning, annotation, and augmentation, followed by training the YOLOv11-Seg model over 351 epochs. The loss function analysis indicates stable convergence, demonstrating the model’s effective learning capabilities. The evaluation results show an mAP@0.5 average of 0.808, F1 Score(B) of 0.8212, and F1 Score(M) of 0.8382, with 81.56% of test samples achieving confidence scores above 90%. The model performs effectively in static scenarios, such as equipment detection in Xiong’an New District, and dynamic scenarios, including real-time monitoring of workers and vehicles, maintaining stable performance even at 1080P resolution. Furthermore, it demonstrates robustness under challenging conditions, including nighttime, non-construction scenes, and incomplete images. The study concludes that YOLOv11-Seg exhibits strong generalization capability and practical utility, providing a reliable foundation for enhancing safety and intelligent monitoring at construction sites. Future work may integrate edge computing and UAV technologies to support the digital transformation of construction management.
He et al. (Tue,) studied this question.