| 徐峰,李建文,张举.基于图像卷积神经网络的湿浆包装机托板缺纸状态识别监测方法[J].中国造纸,2026,45(5):182-189 |
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| 基于图像卷积神经网络的湿浆包装机托板缺纸状态识别监测方法 |
| CNN-based Image Recognition and Monitoring of States on the Pallet Paper Shortage for Wet Pulp Packaging Machines |
| 收稿日期：2025-12-02 修订日期：2025-12-21 |
| DOI：10.11980/j.issn.0254-508X.2026.05.021 |
| 关键词: 图像识别 RTSP流媒体协议 Compact 卷积神经网络 特征向量 |
| Key Words:image recognition RTSP streaming protocol Compact convolutional neural network feature vector |
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| 摘要:为解决湿浆包装机无法准确识别木制托板是否缺纸的问题，本研究提出了一种基于图像卷积神经网络（CNN）识别的监测方法，包括以下步骤：①通过RTSP流媒体协议获取托板正视图像；②对ROI区域进行多次标注，使用Compact网络得出图像→特征映射基准，期间加以图像增强模拟现场光照波动；③对实时图像进行卷积运算，将图片色彩、边缘差异提取为特征向量，并根据特征向量映射规则进行特征向量比对，判断缺纸状态；④通过标签进行状态判断，以TCP协议将状态信息输出到PLC中，告知系统自动更换空托板。相比传统RGB阈值处理，CNN系统识别处理通过Compact轻量化，较好地消除了现场干扰影响，实现了自动更换木制托板，确保了生产过程的连续性和高效率。 |
| Abstract:To address the issue that the wet pulp packaging machine cannot accurately identify paper shortage on wooden pallets, this study proposed a monitoring method based on convolutional neural network (CNN) image recognition, which consists of the following steps: ① Capture front-view images of pallets via the RTSP streaming protocol; ② Perform multiple annotations on the region of interest (ROI), establish an image-to-feature mapping benchmark using a compact network, and apply image enhancement to simulate on-site light intensity fluctuations throughout the process; ③ Conduct convolution operations on real-time images, extract color and edge differences of images as feature vectors, and compare feature vectors following mapping rules to determine the paper shortage status; ④ Output status information to the PLC via the TCP protocol for system-triggered automatic replacement of empty pallets based on classification results. Compared with traditional RGB threshold processing, the lightweight Compact of CNN recognition system effectively mitigated on-site environmental interferences. It enabled automatic replacement of wooden pallets, thereby ensuring continuous and high-efficiency production. |
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