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Multilevel Structural Components Detection and Segmentation toward Computer Vision-Based Bridge Inspection.

Weilei Yu¹, Mayuko Nishio¹

¹Department of Engineering Mechanics and Energy, University of Tsukuba, 1-1-1 Tennodai, Ibaraki, Tsukuba 305-8577, Japan.

Sensors (Basel, Switzerland)

|May 20, 2022

Summary

Computer vision (CV) automates bridge inspection using convolutional neural networks (CNNs). This framework accurately identifies bridge types and components from various distances, enhancing safety and maintenance efficiency.

Keywords:

bridge inspection components segmentation computer vision multilevel detection

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Area of Science:

Civil Engineering
Computer Science
Artificial Intelligence

Background:

Bridge deterioration poses significant safety risks.
Automated inspection methods are crucial for efficient and accurate structural assessments.
Computer vision (CV) offers potential for automating bridge inspection tasks, particularly with unmanned aerial vehicles (UAVs).

Purpose of the Study:

To propose and verify a multilevel bridge inspection framework utilizing CV technology and CNN models.
To assess the performance of different CV models for recognizing bridge types and components at various distances.
To investigate techniques for improving model accuracy, including image augmentation and hyperparameter tuning.

Main Methods:

Developed a multilevel inspection framework using CV and CNNs.
Trained Resnet50 on a dataset of 1200 images for long-distance bridge type classification.
Employed YOLOv3 for medium-distance bridge component detection and Mask-RCNN for close-distance component segmentation.

Main Results:

Achieved 96.29% accuracy in classifying bridge types (arched, cable-stayed, suspension).
Obtained 93.55% detection accuracy for girders and 82.64% for piers at medium distances.
Reached 90.8% bounding box and 87.17% segmentation accuracy for components at close distances.

Conclusions:

The proposed CV framework effectively automates multilevel bridge inspection.
Different CV models demonstrate varying performance based on distance and task (classification, detection, segmentation).
The study provides valuable insights into CV model selection, data acquisition, and optimization for bridge inspection.