A Data-Driven Intelligent System for Mobile Detection of Infrastructure Construction Quality
- 2 December 2024
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 74 (00189456) , 1-13
Abstract
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 the comparison with YOLOv5x and YOLOv8 algorithms, respectively.Keywords
Funding Information
- National Key Research and Development Program of China (2021YFF0500903)
- National Natural Science Foundation of China (52178271, U20A20312)
This publication has 35 references indexed in Scilit:
- End-to-End Structural analysis in civil engineering based on deep learningAutomation in Construction, 2022
- Automated defect inspection of concrete structuresAutomation in Construction, 2021
- Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer visionCement and Concrete Research, 2021
- A deep learning approach for fast detection and classification of concrete damageAutomation in Construction, 2021
- Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learningAutomation in Construction, 2021
- Automated bughole detection and quality performance assessment of concrete using image processing and deep convolutional neural networksConstruction and Building Materials, 2021
- Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learningAutomation in Construction, 2020
- Instance-level recognition and quantification for concrete surface bughole based on deep learningAutomation in Construction, 2019
- SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networksData in Brief, 2018
- A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defectsApplied Surface Science, 2013