Work

Correlation Data Augmentation-Based YOLO-Integrated Object Detection of Thermal-Equalization Video Using Line Scanning Inductive Thermography

Year: 2024

Type: article

Abstract: Active infrared thermography (IRT) in non-destructive testing is an attractive technique used to detect wide areas in real-time on site. Most of the objects inspected on site generally have rough surfaces and foreign substances, which significantly affects their detectability. To solve this problem, in this study, line scanning (LS)-based induction thermography was used to acquire thermal image data of a specimen containing foreign substances. The heat distribution caused by foreign substances was removed using the Gaussian filtering-based Fast Fourier Transform (FFT) algorithm. After that, the data augmentation was performed by analyzing the correlation, and crack detection for the images was performed using you only look once (YOLO) deep learning. This study presents a method for removing non-uniform heat sources using the FFT algorithm, securing virtual data augmentation, and a detection mechanism for moving inspection objects using AI deep learning.

Source: Applied Sciences

DOI: 10.3390/app142411903

Authors: Seung-Ju Lee, Won-Tae Kim, Hyun-Kyu Suh

Institution: Kongju National University

Language: English

Topic: Thermography and Photoacoustic Techniques

Subfield: Mechanics of Materials

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728