A Data-Driven Intelligent System for Mobile Detection of Infrastructure Construction Quality

Detecting construction quality issues is crucial for an infrastructure project. In this study, a data-driven intelligent system is proposed to improve the efficiency of quality detection and management. First, an intelligent system framework including mobile devices, application software, and deep learning algorithms is developed. Second, an improve YOLOv5 (HA-YOLO) based on the hybrid attention is proposed to improve the inference speed and accuracy of defect detection. In addition, an apparent quality dataset from the construction site is analyzed and produced. Finally, the stability of the intelligent detection system is tested under various shooting conditions to demonstrate its effectiveness and robustness. The experimental results on public datasets (NEU-DET) show that HA-YOLO achieves 79.8 mAP with a maximum improvement of 13.8% over other series models. In our task, the HA-YOLO improves the detection accuracy by 2.7% and 3.8% in comparison with the YOLOv5x and YOLOv8 algorithms, respectively.