A deep multi-instance neural network for dyeing-free inspection of yarn dyeing uniformity

Abstract

Dyeing evenness is a decisive factor influencing the commercial values of polyester yarn. Thus, the inspection of yarn dyeing uniformity plays a vital role in the textile community. Due to the inefficiency of dyeing process, the traditional dyeing-based inspection methods are both laborious and time-consuming. To improve efficiency, this study attempts to develop a fast dyeing-free method for dyeing uniformity inspection based on imaging spectrometer and multi-instance learning (MIL). The relevant properties of yarn samples are first recorded in the hyperspectral images (HSIs). As the available labels are ambiguous, classifying the collected HSIs has become a MIL problem. Meanwhile, the correlation between the spectral pixels and the sample labels might be sophisticated. It might be difficult for the existing MIL methods to learn such data. In this paper, a deep Fisher score-based multi-instance neural network (DFSNet) is also proposed for classifying the captured HSIs. The DFSNet is able to learn a sophisticated correlation between deep instance features and bag representation. Specifically, a Fisher score-based MIL pooling layer is first developed to convert the instance-level features into bag-level features. The DFSNet is then developed with a ladder structure and the Fisher score-based MIL pooling layer. The proposed dyeing-free method and DFSNet are evaluated using the actual polyester samples. Moreover, the proposed DFSNet and the spectral range of HSI are further analyzed. The experiment results have indicated that the proposed method could achieve satisfactory performance, providing a potential solution to fast dyeing uniformity inspection.