A A YOLO-Based Machine Learning Framework for Detection of Soft Pneumatic Actuator Bending Angles

The bending angle of soft pneumatic actuator (SPA) is a critical parameter influencing their reliability and effectiveness across various applications. Conventional measurement methods are often labour-intensive and impractical for experiments requiring multiple trials, creating a need for efficient, non-invasive techniques. This study proposes a machine learning framework leveraging YOLO (You Only Look Once) models to detect SPA bending angles from image data, eliminating the need for additional hardware. A comprehensive dataset of SPAs under varying actuation pressures, with meticulously labelled bending angles, was curated to train a YOLO-based regression model. The results highlight the model's strong performance, achieving a recall of 99.1%, precision of 70%, and mean average precision (mAP) scores of 86.42% (IoU 0.5) and 84.35% (IoU 0.5–0.95). Low training and validation losses indicate high accuracy in bounding box predictions, object-background differentiation, and object classification. Optimized learning rates ensured efficient parameter updates, achieving convergence without overfitting. The proposed framework demonstrates a robust balance between accuracy, robustness, and efficiency, making it a practical solution for reliable SPA bending angle detection in real-world applications. This study underscores the potential of machine learning-driven techniques to streamline SPA characterization, offering a scalable and non-invasive alternative to traditional methods.