# Study on the Detection Mechanism of Multi-Class Foreign Fiber under Semi-Supervised Learning
## 1 Introduction  
Cotton is the raw material for producing clothing, towels, quilts, and other necessities. Raw cotton's quality directly influences the quality of cotton textiles, which in turn affects textile prices and the economic performance of cotton textile enterprises [1]. Cotton often becomes intermixed with foreign fibers during growth, harvesting, and subsequent processing. If these foreign fibers cannot be removed in time, they can lead to yarn breakage during spinning and cause blemishes on the cloth surface during weaving. Foreign fibers with dark colors may influence the appearance of light-colored fabrics, while light-colored foreign fibers can result in uneven dyeing. Unremoved foreign fibers have an essential impact on the quality of textiles at all stages of cotton processing, and different foreign fibers have different degrees of harm to textiles. Efficient methods for removing these foreign fibers significantly enhance the overall quality of cotton textiles [2]. In traditional cotton production, almost all textile enterprises still rely on manual sorting for fiber impurity detection [3]. However, this approach suffers from low work efficiency due to human eye exhaust and the striking similarity between foreign and cotton fibers' physical properties. Rapid manual sorting makes it challenging to distinguish foreign fibers from cotton accurately, resulting in slow detection work and increased production time costs. In recent years, the rapid development of artificial intelligence technology, particularly deep learning, has led to significant advancements in foreignfiber detection methods [4; 5; 6; 7; 8]. [PERSON] [4] used the feature selection method to match classifiers and select the optimal feature set for detecting foreign fibers, obtaining excellent performance in foreign fiber detection with Extreme Learning Machine and Kernel Support Vector Machine, which achieved classification accuracies of 93.61% and 93.17% respectively, using feature sets of 42 and 52 features. [PERSON] [5] designed \"Cotton-YOLO\" for the efficient detection of foreign fibers in seed cotton, achieving an accuracy of 99.12%, an mAP50 of 96.92%, and a detection speed of 132.2 FPS (7.6 ms per image), significantly outperforming YOLOV7. [PERSON] [6] improved Faster RCNN for the diversity of foreign fiber size and shape characteristics. The accuracy, precision, recall, and F1 score improved by 3.21%, 0.90%, 2.51%, and 0.017, respectively, after the improvement. [PERSON] [7] proposed a foreign fiber classification model \"CottonNet-Fusion\" based on a residual network with feature difference fitting for the problem that foreign fibers in raw cotton are similar to cotton in terms of features, which maintains the classification accuracy at 90.3% in the complex environment sampled images. [PERSON] [8] proposed an improved object detection and classification algorithm based on the optical and polarization differences between cotton fibers, which achieves the recognition and classification of small foreign fibers with an average identification accuracy of 96.9%. These methods use the advantages of handling large data samples and have successfully detected foreign fibers. However, a common limitation is the heavy reliance on extensive labeled data during training. Additionally, foreign fiber images exhibit diverse features influenced by real-world conditions--for instance: (1) Raw cotton quality problems: Reserve cotton may appear weakly yellow, inconsistent with conventional gray-white cotton. Long-staple cotton, on the other hand, appears milky white, which can be easily mistaken for white polypropylene yarn. (2) Equipment problems: Insufficient processing of raw cotton by the cotton opener can lead to blurred foreign fiber images. Poor lighting conditions further aggravate image darkness [9]. Accurately distinguishing and labeling foreign fibers demands domain-specific knowledge, and manually labeling scattered cotton foreign fibers in images is both time-consuming and labor-intensive.