International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
Automated AI-Based Defect Detection in Aerospace Industry: A Comprehensive Review Afeefa Azam 1, Jahan Aara Ahmed 2, B Shivani 3 1,2,3 Student, Dept. Of Computer Engineering, Stanley College of Engineering and Technology for Women,
Hyderabad, Telangana ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Computer vision-based automated defect
consuming, labor-intensive, and error-prone. To combat these challenges, Artificial Intelligence (AI)-implied defect detection systems have been introduced as a robust substitute, using deep models such as YOLO (You Only Look Once) and Faster R-CNN for automated highaccuracy inspection. Current studies (2022–2024) illustrate notable improvements in AI-powered defect detection, with YOLO-based models reaching 94.5% accuracy in turbine blade coating inspection and Faster RCNN reaching 93.2% accuracy in UAV-based aircraft surface scanning. Most studies, however, center on model performance and not practical deployment, and therefore, create a lack of large-scale, accessible solutions suitable for industry uptake. To fill the gap between theoretical AI models and real-world deployment, we created a pioneering web-based defect detection system that takes advantage of YOLOv8's high accuracy while maximizing accessibility. Our end-to-end solution includes training a bespoke YOLOv8 model on jet engine blade defects, optimizing it for edge deployment through ONNX conversion, and deploying it in a JavaScript backend for real-time browser-based inference. The system includes a web-based, interactive HTML/CSS frontend supported on GitHub Pages, which minimizes the dependency on dedicated equipment such as GPUs or UAVs. In comparison to conventional strategies, our own lightweight and platform-independent implementation allows affordable and scalable inspections through web browsers economically, proving that web-based AI is possible by benchmarking execution against current precision, latency, and deployment-related solutions. This research moves the field forward by bringing real-time, AI-based defect detection closer to aerospace applications.
detection in aerospace parts, including jet engine turbine blades and aircraft surfaces, is safety- and maintenancecritical. Recent deep learning breakthroughs, especially YOLO-based models (e.g., YOLOv5, YOLOv8) and Faster RCNN, have demonstrated high accuracy (up to 94.5%) in identifying coating cracks, corrosion, and structural defects. Yet, most research considers only model accuracy, ignoring the deployment challenges in real-world applications. This review fills the gap by comparing state-of-the-art object detection techniques with a web-based deployment of YOLOv8 for real-time defect detection. Our methodology trains a tailor-made YOLOv8 model on turbine blade defect data, optimizes it through ONNX runtime, and deploys it to the browser using JavaScript with a HTML/CSS frontend, hosted on GitHub Pages. This light-weight solution dispenses with the need for specialized hardware (e.g., UAVs or servers), with low-latency inference performed directly within web environments. We contrast our work with 12 foundational studies (2018–2024), emphasizing trade-offs between accuracy, recall, F1-scores, and deployment efficiency. Major findings show that YOLOv8 surpasses previous architectures (82.3% mAP in YOLOv3) and competes with Faster R-CNN (93.2% in UAV-based systems), while our web integration provides unparalleled accessibility. Issues such as browser memory capacity and cross-platform compatibility are addressed, together with the future of edge-AI in aircraft maintenance. This work highlights the prospects for ONNX-optimized models and web-based deep learning for democratizing defect detection across aerospace and other industries.
Key Words: Edge AI, Defect detection, YOLOv8, ONNX, JavaScript deployment, aerospace inspection, deep learning, web-based AI, turbine blades, realtime object detection.
2. LITERATURE REVIEW A. Defect Detection in Jet Engine Components
1.INTRODUCTION
Deep learning-based defect detection for jet engine turbine blades has seen significant breakthroughs in inspection accuracy and reliability in recent research. The study [1] set a new standard by proving that YOLO-based techniques were capable of achieving remarkable 94.5% accuracy in detecting coating defects, a far leap from conventional computer vision techniques. This research specifically emphasized the model's ability to identify micron-scale cracks and delamination in thermal barrier
The aerospace sector depends greatly on the integrity of structural components like jet engine turbine blades and airplane surfaces. Catastrophic failures can result if even slight defects—like cracks in coatings, corrosion, or manufacturing defects—go undetected. The conventional inspection process, which entails manual visual inspections or non-destructive testing (NDT), is time-
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