Detectable Inspection: Propeller Nick Detection Model Development Using YOLOv8

Purpose: Nicks in the propeller can lead to catastrophic failures if not repaired properly, but proper detection can be difficult. To improve nick inspection tasks, the researchers intend to develop a nick detection model using the object detection algorithm, You Only Look Once. Design/Methodology/Approach: The researchers select YOLOv8 algorithm. To develop a nick detection model, the researchers collected 4423 nick images in various conditional variable settings. Using preprocessed 1800 nick images, six models are trained with different hyperparameter settings and assessed for performance. Findings: A well‐trained model achieved 0.986 precision, 0.982 recall, and 0.984 F 1 score. Additionally, the researchers assessed explainability analysis using Grad‐cam to see if this model properly extracts nick features during the training process. Originality/Value: This research contributes to improving inspectors’ propeller nick inspection maturity using this nick detection model. Therefore, this study can potentially reduce general aviation accidents caused by propeller failure.