Robust Cockpit Panel Image Processing for Shape Analysis Using Deep Learning-Based Shape Classification and Transfer Learning

This chapter explores the challenges and solutions in cockpit panel image processing for shape analysis. It introduces a hybrid learning framework that combines deep learning and transfer learning to overcome data scarcity issues in aviation applications. The study presents a novel approach that achieves 100% accuracy in classifying real-time shapes from actual cockpit panels, outperforming traditional rule-based geometric methods. The methodology involves dataset preparation, CNN architecture design, and transfer learning, demonstrating adaptability to real-world conditions. The results highlight the robustness of the proposed method, making it suitable for real-time aviation monitoring systems. The chapter also discusses the limitations of traditional methods and the advantages of deep learning in handling complex, real-world scenarios. The conclusion emphasizes the model's high accuracy and its potential for future applications in aviation-related shape classification tasks.