An Efficient and Effective Automatic Recognition System for Online Recognition of Foreign Fibers in Cotton

Abstract

Preventing foreign fibers from being mixed with cotton is essential for producing high-quality cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This paper proposes an efficient recognition system to accurately recognize foreign fibers mixed in cotton. The core component of the proposed system is an efficient classifier based on the kernel extreme learning machine (KELM). A two-step grid search strategy, which integrates a coarse search with a fine search, is adopted to train an optimal KELM recognition model. The resultant model is compared with the support vector machine and extreme learning machine on a real data set using tenfold cross-validation analysis. The experimental results show that the proposed recognition system can achieve classification accuracy as high as 93.57%, which is superior to the other two state-of-the-art systems. The results clearly confirm the superiority of the developed model in terms of classification accuracy. Promisingly, the proposed system can serve as a new candidate of powerful foreign fiber recognition systems with excellent performance.