Work

Welding defect detection with image processing on a custom small dataset: A comparative study

Year: 2024

Type: article

Abstract: This work focuses on detecting defects in welding seams using the most advanced You Only Look Once (YOLO) algorithms and transfer learning. To this end, the authors prepared a small dataset of images using manual welding and compared the performance of the YOLO v5, v6, v7, and v8 methods after two‐step training. Key findings reveal that YOLOv7 demonstrates superior performance, suggesting its potential as a valuable tool in automated welding quality control. The authors’ research underscores the importance of model selection. It lays the groundwork for future exploration in larger datasets and varied welding scenarios, potentially contributing to defect detection practices in manufacturing industries. The dataset and the code repository links are also provided to support our findings.

Source: IET Collaborative Intelligent Manufacturing

DOI: 10.1049/cim2.70005

Authors: József Szőlősi, Béla J. Szekeres, Péter Magyar, Bán Adrián, Gábor Farkas, Mátyás Andó

Institution: Eötvös Loránd University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,290

Funder: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap