Intelligent Defect Detection for Aircraft Composite Materials: Integrating Infrared Thermography and YOLO-Based Deep Learning

This paper presents a defect detection system for aircraft composite materials that integrates active infrared thermography with a lightweight YOLO11n deep learning model. The proposed approach utilize the heat conduction behavior described by Fourier’s Law to identify subsurface defects such as delamination and disbonds by visualizing heat flow anomalies under controlled thermal excitation. To enhance mobility and practical deployment, the system combines portable thermal cameras, UAV-mounted thermal imaging, and real-time detection pipelines implemented through a custom iOS application. Results show that the mobile detection pipeline achieves real-time performance with an average speed of approximately 24 FPS, while UAV inspections are limited by battery life and network transmission delays. This study demonstrates the feasibility of combining thermography with mobile deep learning to support flexible, efficient inspections during routine base maintenance and highlights key areas for future optimization.