What question did this study set out to answer?

To develop a structured classification system for aircraft surface defects and a corresponding detection platform.

March 21, 2026

Towards an Intelligent Aircraft Surface Inspection System: Image-Based Recognition and Classification of Structural Defects

Key Points

To develop a structured classification system for aircraft surface defects and a corresponding detection platform.
Analysis of various surface defects including corrosion and cracks.
Creation of a Classification Catalogue based on literature review and standards.
Utilization of Roboflow for training algorithms to detect surface defects using CNNs.
Image acquisition and data augmentation to enhance model performance.
Expert validation of annotations for precision and accuracy.
Development of a Defect Classification Catalogue for consistent identification of surface anomalies.
Establishment of the Aircraft Inspection Repository (AIR) for data management and visualisation.
Improvement in model generalisability and accuracy through data augmentation techniques.

Abstract

With the growth and modernisation of the air transport sector and the aviation industry, ensuring the structural integrity of aircraft has become paramount for maintaining aviation safety, minimising unexpected downtime, and adhering to regulatory standards. Surface anomalies, such as corrosion, cracks, and paint degradation, can significantly affect an aircraft's airworthiness. This study proposes a detailed analysis of various surface defects, classified by type, affected area, and material, to develop a consistent Classification Catalogue. This catalogue will serve as the foundation for creating a structured findings database using Roboflow, which will be employed to train models for the automatic detection and classification of aircraft surface defects using advanced image processing and machine learning techniques, with a particular focus on convolutional neural networks (CNNs). The key innovation of this research lies in the development of a validated image dataset structured around a comprehensive and standardised Defect Classification Catalogue. This catalogue has been compiled through a rigorous review of scientific literature and industrial standards, ensuring terminological consistency, improving annotation accuracy, and facilitating effective model training. A methodological pipeline is introduced, which includes: 1) Image acquisition from open-source databases, industrial archives, and controlled field inspections, 2)Data augmentation techniques (e.g., rotation, noise injection, and cropping) to improve model generalisability, 3) Manual annotation guided by the established catalogue, and 4) Expert validation to ensure the precision and completeness of annotations. To support defect traceability and facilitate integration into predictive maintenance workflows, a centralised platform, the Aircraft Inspection Repository (AIR), has been developed. AIR enhances accessibility, supports interactive data visualisation, and provides access to inspection data along with their corresponding reports, thereby promoting dynamic decision-making and long-term structural monitoring.

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Towards an Intelligent Aircraft Surface Inspection System: Image-Based Recognition and Classification of Structural Defects

Key Points

Abstract

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