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Development and Implementation of a Defect Detection Model for Microstructures Using Image Processing Methods.

Sandra Gajoch¹, Dorota Wilk-Kołodziejczyk^1,2, Łukasz Marcjan¹

¹Department of Applied Computer Science and Modelling, Faculty of Metals Engineering and Computer Science, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Krakow, Poland.

Materials (Basel, Switzerland)

|November 27, 2025

Summary

This summary is machine-generated.

This study developed AI models for defect detection in austempered ductile iron (ADI) microstructures. ResNet, UNet, and YOLO models offer distinct advantages for classification, detailed analysis, and industrial defect detection.

Keywords:

ResNet YOLO austempered ductile iron cast iron

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Area of Science:

Materials Science
Computer Science
Artificial Intelligence

Background:

Defect detection in austempered ductile iron (ADI) microstructures is crucial for quality control.
Traditional methods for defect analysis can be time-consuming and subjective.

Purpose of the Study:

To develop and implement artificial intelligence (AI) models for automatic defect detection in ADI microstructures.
To compare the performance of different machine learning architectures (ResNet, UNet, YOLO) for this task.

Main Methods:

Utilized three machine learning approaches: image classification (ResNet), pixel-wise segmentation (UNet), and object detection (YOLO).
Data preparation involved k-means clustering, morphological operations, binary mask generation, label conversion, and data augmentation.
Models were adapted and tested on a custom dataset of ADI microstructural images.

Main Results:

ResNet provided high classification accuracy but lacked defect localization.
UNet generated precise segmentation masks for quantitative analysis but required substantial computational resources and struggled with small defects.
YOLO enabled rapid defect detection using bounding boxes.

Conclusions:

Each AI model demonstrated unique strengths for specific applications in ADI microstructure defect analysis.
ResNet is suitable for preliminary classification, UNet for detailed laboratory analysis, and YOLO for industrial detection tasks.