Reliable identification of shapes in aircraft cockpit panels is critical for ensuring operational safety and supporting automated fault detection systems. Though effective for basic shapes, traditional rule-based methods tend to underperform when faced with variable image quality or orientation. This paper presents a deep learning-based framework enhanced by transfer learning to address these limitations. A convolutional neural network (CNN) using a hybrid dataset comprising 40,000 generic geometric images and 8,000 cockpit-specific samples generated via augmentation. The model’s performance is significantly improved by fine-tuning it on domain-specific data, achieving 100% classification accuracy during validation. The proposed method enhances the accuracy, adaptability, and robustness in real-world cockpit scenarios. It outperforms traditional methods.