Micro-KTNet: Microstructure knowledge transfer learning for fiber masterbatch agglomeration recognition

Fiber masterbatch production suffers from inherent agglomeration effect of high-concentration color masterbatches, negatively impacting color uniformity, particle dispersion, and thermal stability in fiber masterbatch, and ultimately the quality of fiber products. However, accurately recognizing and classifying the agglomeration in scanning electron microscopy (SEM) images remain a challenge due to the complexity of agglomeration status, difficulty in distinguishing micro-size agglomeration, and limited data availability. To address this challenge, this paper proposes a novel microstructure recognition architecture, named Micro-KTNet, which is designed for segmenting SEM images of fiber masterbatch aggregation. First, Micro-KTNet leverages transferable microstructural features to initialize the proposed network, mitigating the impact of limited data. Then, to handle multi-scale features in the microstructure, an encoding-decoding structure is constructed and skip connections are used to transfer the output features of the encoding layer. Finally, the decoder incorporates a novel attention module to effectively process both global and local features. To evaluate Micro-KTNet, a unique fiber masterbatch SEM database is built as the benchmark for micro-size agglomeration recognition. Experimental results indicate that Micro-KTNet surpasses existing state-of-the-art methods and improves the recognition precision from 75. 37% to 87. 04%.