Investigation of AI Algorithms for Photometric Online Analysis in a Draft Tube Baffle Crystallizer - Laura Marsollek; Julius Lamprecht; Norbert Kockmann (Crystals)
The rapid advancement of AI algorithms presents new opportunities for sensing technologies based on image recognition, such as real-time crystallization monitoring. This work investigates the use of computer vision to detect and size crystals in a lab scale draft tube baffle crystallizer (DTBC). A specially developed analytical bypass was implemented on the DTBC to enable a low-influence analysis without invasive intrusion into the process. By utilizing AI models such as YouOnlyLookOnce version 8 (YOLOv8), YOLOv8 Segmentation (YOLO8seg), and the convolutional network for biomedical image segmentation U-Net, this study assesses their effectiveness in determining crystal size distributions from photometric images. While U-Net was deemed unsuitable due to computational demands and accuracy issues, YOLOv8 and YOLO8seg performed better in terms of efficiency and precision. YOLO8seg, specifically, achieved the highest accuracy, with a mean average precision (mAP) of 82.3%, and excelling in detecting larger crystals, but underperforming with crystals smaller than 90 µm. Despite this limitation, YOLO8seg was able to compete with the manual methods and was superior to the state-of-the-art algorithm mask region convolutional neural network (Mask R-CNN) in terms of accuracy. The study suggests that specific training and adaptation of the imaging conditions could further improve the crystal detection performance.