Smart Borescope Inspection System with AI-Based Defect Detection for Aero-Engine Maintenance

Authors:

Abishek K, Abhishek AS, Aswathi E, Kushi Jain, Shon Sony, Kaushal Kumar

Abstract - This Aircraft engine maintenance is a critical component of aviation safety and operational reliability. Traditional borescope inspections depend heavily on human expertise, making the process time-consuming and prone to subjective errors. This paper proposes a Smart Borescope Inspection System integrated with Artificial Intelligence (AI) and Computer Vision techniques for automated defect detection in aero-engine components. The proposed system utilizes a high-resolution borescope camera, image preprocessing techniques, and a YOLOv8-based deep learning model to identify defects such as cracks, corrosion, dents, blade burns, and foreign object damage (FOD). Experimental evaluation demonstrates improved inspection accuracy, reduced inspection time, and enhanced maintenance decision-making. The proposed framework supports predictive maintenance and contributes toward intelligent aviation maintenance systems.

Keywords: Smart Borescope, Defect Detection, Computer Vision, YOLOv8, Predictive Maintenance, Aero-Engine Inspection, Deep Learning, Aviation Safety