基于改进BiSeNet的片烟加料机雾化效果实时监测系统OA

To realize the real-time monitoring of the casing atomization effect of nozzles during the tobacco strip casing process,a real-time visual monitoring system based on deep learning was designed.The system integrates image acquisition module,image processing module and online alarm module to acquire real-time images of nozzle atomization.The image processing module extracts atomization features by morphological processing and an improved lightweight semantic segmentation model(LightBiSeUNet).Based on an asymmetric dual-path parallel architecture comprising a detail branch and a semantic branch,the model effectively suppresses complex interferences,such as the bright background of the cylinder by introducing lightweight depthwise separable convolution(LightConv),Squeeze-and-Excitation(SE)channel attention mechanism,and global average pooling-based global context enhancement(GAP-based GCE)module,achieving adaptive focusing on irregular atomization edges.Experimental results showed that:1)The LightBiSeUNet model achieved a mean intersection over union(MIoU)of 99.57%with a parameter count of about 0.110 M,enabling precise segmentation of atomization images captured by a high-speed camera frame by frame.2)Comparisons with U-Net,MobileUNet and BiSeNet further verified its advantages in segmentation accuracy and resistance to overfitting.3)The application in the industrial production line of Liuzhou Cigarette Factory indicated that the closed-loop control based on high-frequency feedback from this vision system enabled the casing cylinder to intervene in transient anomalies in seconds,reducing the coefficient of variation of casing uniformity from 4.80%to 2.24%.This method enables real-time and accurate monitoring of atomization effect and provides a new approach for real-time and lightweight deployment of industrial image segmentation.