Real-Time Detection of Electrohydrodynamic Atomization Modes via a YOLOv8-Based Deep Learning Model

A YOLOv8-based deep learning model was developed to address real-time detection and dynamic regulation needs of the electrohydrodynamic atomization process. An EHDA experimental system was built to obtain images of six typical atomization modes, forming a dataset with 6000 images. After annotation and mosaic augmentation, the dataset served as the training data for the model. The YOLOv8 adopts a “backbone-neck-head” architecture to extract and fuse features, decouple classification and detection, and optimize performance. Experimental results demonstrate that on the test set, the model attains a precision value, recall rate, and mAP50 of 0.995, alongside an mAP50-95 of 0.8. Additionally, its prediction accuracy exceeds 0.99 across all operational modes. Compared with 10 models, it has the best precision and mAP50, as well as low computational complexity, combining high accuracy and lightweight advantages, which can be effectively used for real-time detection of EHDA modes.