The accurate and rapid detection and recognition of jet features are key to dynamic monitoring and online control of the electrospinning process. In this study, a real-time recognition system based on OpenCV was introduced into a coaxial laser-assisted electrospinning system to solve the difficulties of accurate jet recognition and to promote an image processing algorithm response. The jet images with laser assistance were more clearly visible than those without laser assistance, and a significant contrast in grayscale levels existed in the jet image to help distinguish jet features. Subsequently, separate algorithms were designed for the jet visible length calculation, and the recognized visible length of the jet and algorithm running speed were compared. The average visible length of the jet with laser assistance was 11.49 mm, which increased by 1.59 mm compared to that without laser assistance. In addition, the running time of the algorithm with laser assistance was 24.89 ms, reduced by 14.84 ms compared to that without laser assistance, indicating the effectiveness of laser assistance to promote the accuracy and running speed of the jet image recognition process. Additionally, real-time detection of the jet angles was achieved to identify instances of excessive deflection during the electrospinning process. Overall, this study has significant potential to promote the dynamic monitoring of an electrospinning jet.
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