Automated Swiftlet Nest Arch Height Measurement Using YOLO11n-Seg

This study develops an automatic system for measuring the arch height of swiftlet nests based on computer vision with the YOLO11n-seg model. This system overcomes the limitations of manual measurement at PT. Waleta Asia Jaya, where the arch height is one of the characteristics used to determine the quality of the product. The dataset contains 300 images captured using ESP32-S3 CAM (resolution 640×480). Images in the dataset is preprocessed using red channel extraction, intensity-based cropping, background correction, and morphology processing. The nest contour is analyzed using polar transformation and peak detection. The evaluation includes four approaches such as No Smoothing Filter, Ramer-Douglas-Peucker (RDP) Filter, Median Filter (MedFilt), and Savitzky-Golay (SavGol) Filter. Statistical analysis shows that the No Smoothing approach provides the best accuracy with a Root Mean Square Error (RMSE) of 2.28 millimeters as well as a time saving of 78.26% compared to manual measurement. The proposed system offers a time-efficient alternative to manual measurement while maintaining high accuracy in arch height estimation.