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Microcracking in Concrete

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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...

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Updated: Jun 12, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

A lightweight detection algorithm for tooth cracks in optical images.

Zewen Xie¹, Xian Hu², Lide Guo³

¹School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China; School of Physics and Material Science, Guangzhou University, Guangzhou, 510006, China.

Computers in Biology and Medicine

|September 17, 2024

Summary

This summary is machine-generated.

This study introduces an improved object detection algorithm for identifying cracked teeth, significantly reducing model size for easier deployment. The optimized model aids dentists in detecting subtle tooth cracks, improving early diagnosis.

Keywords:

Cracked tooth detection Lightweight algorithm Object detection Optical image YOLOv8

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Last Updated: Jun 12, 2025

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Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision

Published on: April 29, 2014

Area of Science:

Dental diagnostics
Computer vision
Artificial intelligence

Background:

Cracked tooth syndrome (CTS) is a common cause of tooth loss, often presenting with subtle early symptoms.
Accurate and early detection of dental cracks is crucial for effective treatment and preventing tooth loss.

Purpose of the Study:

To investigate an improved object detection algorithm for automatically detecting cracks in dental optical images.
To develop a lightweight and deployable model for clinical use.

Main Methods:

A dataset of over 3000 dental images, including real clinical images, was utilized.
The YOLOv8 object detection model was improved through techniques like model pruning and backbone replacement.
The impact of image enhancement and attention modules on model performance was analyzed.

Main Results:

Model pruning reduced parameters by 16.8% and GFLOPs by 24.3% with minimal performance impact.
Replacing the backbone network was less effective than pruning for this specific task.
Image enhancement and attention modules had minimal impact on YOLOv8 performance for tooth crack detection.

Conclusions:

An improved, lightweight object detection algorithm was developed for detecting tooth cracks.
The optimized model is suitable for deployment on embedded devices, assisting dental professionals.
This technology has the potential to enhance the early identification of cracks on tooth surfaces.