Classification and Captioning of Aircraft Damage Detection Using Machine Learning Models

Abstract

Aircraft structural integrity is a critical determinant of aviation safety, necessitating systematic inspection and prompt identification of defects including cracks, dents, corrosion, and surface deformations. Conventional inspection methodologies depend extensively on manual visual assessment conducted by certified engineers—a process that is inherently time-intensive, labor-demanding, and susceptible to human error. As modern aircraft systems grow in complexity and maintenance operations face increasing pressure for rapid turnaround, the adoption of intelligent, automated inspection solutions has become imperative.

This research proposes a machine learning framework for automated aircraft damage detection and semantic caption generation using deep learning techniques. The system employs Convolutional Neural Networks (CNNs) for autonomous feature extraction and damage classification from aircraft imagery. Complementing the visual analysis, a Natural Language Processing (NLP) module—implemented using Long Short-Term Memory (LSTM) networks and Transformer-based architectures—generates contextually meaningful textual descriptions of identified damage.

Transfer learning is leveraged through pretrained architectures including VGG16, ResNet50, and EfficientNet to enhance accuracy and minimize training overhead. The integrated pipeline—encompassing image preprocessing, feature extraction, multi-class classification, and caption synthesis—produces both visual and semantic outputs. Experimental results demonstrate classification accuracy exceeding 94% alongside coherent caption generation, evaluated using BLEU, ROUGE, and METEOR metrics. The proposed approach advances the efficiency and reliability of aircraft maintenance inspection while enabling automated report generation that reduces human workload and improves maintenance decision-making.