Online monitoring of green pellet size distribution in haze-degraded images based on VGG16-LU-net and haze judgement
Jiaxu Duan, Xiaoyan Liu
Abstract
Online monitoring of pellet size distribution (PSD) of green pellets is an important work in product quality control of pelletization process. Conventionally, image segmentation technique is a preliminary step in computer vision-based PSD monitoring. However, haze, pellets overlapping, and uneven illumination contribute to the main challenges that severely impair the segmentation performance and PSD measurement accuracy. This article proposed a fully automatic online PSD monitoring method incorporating a K-means clustering-based haze judgment module, a lightweight U-net segmentation model with the fusion of none-weight VGG16 features (VGG16-LUnet), and a convex-hull detection and ellipse fitting model for adhesive pellet separation and contour fitting. The VGG16-LUnet model can accurately segment the pellets from both hazy and haze-free images with the help of haze judgment module. Thus, this model can be called VGG16-LUnet-TAdj. Then, a contour fitting model is applied to determine the pellets sizes based on the segmentation results, and the PSD is obtained as well. Extensive experiments on the segmentation of in situ captured green pellet images and the corresponding PSD curves demonstrate that our proposed method performs comparable or even favorable to the state-of-the-art methods.