A Multi-Branch Deep Learning Framework with Frequency–Channel Attention for Liquid-State Recognition
Minghao Wu, Jiajun Zhou, Shuaiyu Yang, Hao Wang, Xiaomin Wang, Haigang Gong, Ming Liu · Electronics · 2025
In the industrial production of polytetrafluoroethylene (PTFE), accurately recognizing the liquid state within the coagulation vessel is critical to achieving better product quality and higher production efficiency. However, the complex and subtle changes in the coagulation process pose significant challenges for traditional sensing methods, calling for more reliable visual approaches that can handle varying scales and dynamic state changes. This study proposes a multi-branch deep learning framework for classifying the liquid state of PTFE emulsions based on high-resolution images captured in real-world factory conditions. The framework incorporates multi-scale feature extraction through a three-branch network and introduces a frequency–channel attention module to enhance feature discrimination. To address optimization challenges across branches, contrastive learning is employed for deep supervision, encouraging consistent and informative feature learning. The experimental results show that the proposed method significantly improves classification accuracy, achieving a mean F1-score of 94.3% across key production states. This work demonstrates the potential of deep learning-based visual classification methods for improving automation and reliability in industrial production.