An Image Processing-Based Approach for Reading Needle-Type Instruments on Aircraft

For guaranteeing the safe and effective functioning of aircraft, image processing techniques can be a valuable tool to detect and evaluate aircraft panel values. In the pursuit of this objective, a dataset covering multiple aircraft models, various sessions, and different lighting conditions was compiled. Four tasks were examined through comparative analysis: object detection, display classification, needle masking, and needle angle detection. YOLOv8 demonstrated high performance in object detection and classification. In the classification task, the adaptability of needle -type device reading was examined by using the well -established models VGG16, Mobilenet V2, and Xception. Denoising autoencoder, U -net, and GrabCut methods were examined for needle masking, and the least squares method was applied to detect needle angle. As we move from the proof -of -concept phase to envisioning the development of an end -to -end system, this work provides significant analysis of image processing methodologies for reading aircraft dashboards.