Intelligent Monitoring Technology for Traffic Road Construction Quality under Deep Learning

Authors

  • Xi Chen Department of Civil Engineering, Shijiazhuang Tiedao University Sifang College, Shijiazhuang 051132, Hebei, China
  • Limin Zhang Department of Civil Engineering, Shijiazhuang Tiedao University Sifang College, Shijiazhuang 051132, Hebei, China

Abstract

Traffic road construction is an important component of urban development. In response to the low accuracy of crack detection in traffic road construction and the difficulty in adapting to complexroad construction scenarios, in this study, the Mask R-CNN (Mask Region-based Convolutional Neural Network) model combined with the SCSE (Spatial Channel and Squeeze Excitation) attention mechanism was adopted to study the intelligent monitoring of the quality of traffic road construction. Firstly, drones equipped with high-definition cameras were used for real-time construction data collection, integrated RI datasets, and then Mask R-CNN models were used to capture crack features. The SCSE attention mechanism module was introduced before the output of the mask branch to help the model better utilize feature information and improve the detection of crack-related features for the monitoring of construction quality. Finally, the Mask R-CNN SCSE model was embedded into the monitoring system. The experimental results showed that the recognition accuracy of the Mask R-CNN SCSE model reached 98.85%, which was 1.08% higher than the Mask R-CNN model. The mAP (mean average precision) reached 94.38%, which improved the accuracy of crack detection in traffic construction. Moreover, it can adapt to different light intensities, weather conditions, and road conditions.

Keywords: traffic road, intelligent monitoring of construction quality, crack detection, deep learning, SCSE attention mechanism

Cite As

X. Chen and L. Zhang, "Intelligent Monitoring Technology for Traffic Road Construction Quality under Deep Learning",
Engineering Intelligent Systems, vol. 33 no. 1, pp. 77-87, 2025.




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Published

2025-01-01

Issue

Vol. 33 No. 1 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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