# An Automatic Foreign Matter Detection and Sorting System for PVC Powder
## 1 Introduction
[PERSON] and [PERSON] [5] proposed a continuous supply of quartz powder raw material to take the color images for quartz using a charge-coupled device (CCD) with a scattered light in a certain wavelength. They found that quartz powder showed a polygonal glass-like surface. Additionally, quartz powder can be identified by the shadow of the polygon edge with the existence of light-yellow foreign matter. [PERSON] et al. [6] used near-infrared spectroscopy (NIRS) to take images of skimmed milk powder to investigate the distribution of inorganic nutritional additives, zinc sulfate and lactose, and melamine with principal component analysis (PCA), correlation analysis, regression analysis, and other techniques. [PERSON] and [PERSON] [7] took images of ultra-high molecular weight polyethylene (UHMWPE) powder with CCD to distinguish foreign matter from UHMWPE powder based on the characteristics of color, size, and density, and they removed this foreign matter through a vacuum or mechanical sorting system. [PERSON] et al. [8] analyzed the Compton scattering image with X-Ray scattering imaging technology. They found that the normalized density value of foreign matter is higher than that of milk powder, such that the images of foreign matter appear to be brighter. [PERSON] et al. [9] used NIRS to capture hyperspectral images for classification with PCA and soft independent modeling of class analogy (SIMCA). In their experiment, wood plastic mixed with contaminants was tested. A second sorting step for the purified fraction helped to further reduce the contamination level, and a final purity of 98.9% was obtained. [PERSON] et al. [10] utilized images of freeze-dried powder captured by a CCD in a back-propagation neural network (BPNN) and support vector machine (SVM) for foreign matter classification with PCA for feature extraction. Their experimental results showed that the recognition rate based on the PCA and SVM algorithms is higher than that based on PCA and the third-nearest neighbor classification algorithm. Later, [PERSON] et al. [11] employed the same digital image technology as [PERSON] et al. [10], with 3 \(\times\) 3 median filtering for pre-processing, and they demonstrated that integrating PCA and SVM could be used to classify foreign bodies in lyophilized powder. [PERSON] et al. [12] utilized computed tomography (CT) to take image of metal powder in an inclusion module receiving samples from a powder source. These 2D images could then be inspected for foreign objects through the segmentation process. After the reconstruction process, 3D images could be established to measure and mark the size or shape of foreign objects. Based on the literature mentioned above, there have been several applications in the development of imaging technology. In addition to observing the distribution of foreign matter in powder particles, the counting or measuring of foreign matter, as well as identification, are performed. However, there will be problems encountered because the operator employs the existing powder inspection mechanism based on the patents mentioned above, or on research articles and existing commercial powder inspection machines ([PERSON] [13]; QICPIC, Sympatec GmbH, Clausthal-Zellerfeld, Germany):  
* Manual annotation is a time-consuming process, and the criteria for foreign matter are inconsistent. Before training the model, the collected foreign matter samples need to be manually annotated as the ground truth of the training sample. However, the operators usually do not have enough time to perform time-consuming annotation work in the on-site environment. Additionally, the annotation process varies from person to person due to the variations of foreign objects, inconsistent particle sizes, or similar characteristics between categories. * Manual feature characterization is time-consuming and a poor adaption: all the methods mentioned earlier utilize the features of the object to identify whether or not the foreign matter is embedded in the powder.