Online Defect Detection in LGA Crystallization Imaging Using MANet-Based Deep Learning Image Analysis

Abstract

In this paper, a MANet-based image detection approach is designed to inspect crystal defects during the cooling crystallization process, like that involving β-form L-glutamic acid (LGA), utilizing an online imaging device. The steps in the presented strategy encompass crystal image preprocessing, crystal image segmentation, and crystal classification. Firstly, the guided image filter is introduced to preprocess the collected crystallization images for offline training and online detection. Then, by using an image augmentation strategy to enlarge the number of crystal image samples for training, the MANet-based network is improved for crystal image segmentation. Accordingly, by defining some features, needle-like crystals can be categorized into four types with an efficient classifier for the detection of normal and defective crystals. The experimental results for the batch crystallization of β-form LGA are provided to illustrate the validity of the presented detection methodology.