Abstract :

Aircraft Structural Inspection Is A Critical Safety Procedure In Aviation Maintenance. Traditional Inspection Techniques Rely Heavily On Manual Visual Examination, Which Is Time-consuming, Expensive, And Prone To Human Error. With Advancements In Deep Learning And Computer Vision, Automated Inspection Systems Have Become Viable Alternatives. This Project Presents A Deep Learning-based Aircraft Inspection System Using Convolutional Neural Networks (CNN) For Feature Extraction And YOLO (You Only Look Once) For Real-time Object Detection. The System Detects Surface Defects Such As Cracks, Corrosion, Dents, And Paint Damage From Aircraft Images. The Model Is Trained On Annotated Datasets And Evaluated Using Performance Metrics Such As Accuracy, Precision, Recall, F1-score, And Mean Average Precision (mAP). Experimental Results Demonstrate That YOLO-based Detection Provides High-speed Real-time Performance With Strong Localization Capability, While CNN Achieves High Classification Accuracy. The Proposed System Significantly Reduces Inspection Time And Enhances Reliability In Aircraft Maintenance Procedures.

Published:

06-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

42-48

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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