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Detection of Missing Bolts for Engineering Structures in Natural Environment Using Machine Vision and Deep Learning.

Zhenglin Yang¹, Yadian Zhao², Chao Xu¹

¹School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China.

Sensors (Basel, Switzerland)

|July 8, 2023

Summary

This summary is machine-generated.

A new machine vision and deep learning method accurately detects missing bolts in structures. This automated solution enhances safety management for bolted connections, proving effective even in real-world scenarios.

Keywords:

bolt loosening deep learning machine vision object detection structural health monitoring

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Area of Science:

Engineering
Computer Science
Artificial Intelligence

Background:

Accurate detection of missing bolts is critical for structural integrity and safety.
Existing methods may lack efficiency, accuracy, or adaptability to varied conditions.

Purpose of the Study:

To develop an automated, accurate, and efficient method for detecting missing bolts in engineering structures.
To leverage machine vision and deep learning for enhanced bolt detection.

Main Methods:

A comprehensive dataset of bolt images was created for model training.
Compared deep learning models (YOLOv4, YOLOv5s, YOLOXs), selecting YOLOv5s for bolt target detection.
Implemented a missing bolt detection approach using perspective transformation and Intersection over Union (IoU).

Main Results:

The YOLOv5s model achieved high average precisions for bolt heads (0.93) and nuts (0.903).
The proposed method accurately identified bolt targets (>80% confidence) under diverse conditions (distance, angle, light, resolution).
Successfully detected missing bolts on a real footbridge structure, even from 1 meter.

Conclusions:

The developed method offers a low-cost, efficient, and automated solution for monitoring bolted connections.
The approach demonstrates feasibility and effectiveness in real engineering applications.
Enhances safety management by providing reliable missing bolt detection.