Deep Learning-Based Method for Accurate Real-Time Seed Detection in Glass Bottle Manufacturing

Glass bottle-manufacturing companies produce bottles of different colors, shapes and sizes. One identified problem is that seeds appear in the bottle mainly due to the temperature and parameters of the oven. This paper presents a new system capable of detecting seeds of 0.1 mm2 in size in glass bottles as they are being manufactured, 24 h per day and 7 days per week. The bottles move along the conveyor belt at 50 m/min, at a production rate of 250 bottles/min. This new proposed method includes deep learning-based artificial intelligence techniques and classical image processing on images acquired with a high-speed line camera. The algorithm comprises three stages. First, the bottle is identified in the input image. Next, an algorithm based in thresholding and morphological operations is applied on this bottle region to locate potential candidates for seeds. Finally, a deep learning-based model can classify whether the proposed candidates are real seeds or not. This method manages to filter out most of false positives due to stains in the glass surface, while no real seeds are lost. The F1 achieved is 0.97. This method reveals the advantages of deep learning techniques for problems where classical image processing algorithms are not sufficient.This work was partially supported by OPENZDM project. This is a project from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101058673 in the call HORIZON-CL4-2021-TWIN-TRANSITION-0

Spartan Daily, November 2, 1945

Volume 34, Issue 22https://scholarworks.sjsu.edu/spartandaily/3658/thumbnail.jp

Montana Kaimin, September 25, 2012

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/6609/thumbnail.jp

Montana Kaimin, September 25, 2012

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/6609/thumbnail.jp

Real-time Product Quality Inspection Monitoring System using Quadratic Distance and Level Classifier

Automated product quality inspection has become a very important process in industries to maintain high product efficiency. This paper presents a real-time product quality inspection monitoring system for beverages product. The proposed system used Internet Protocol (IP) camera to capture the image of the bottle through computer network in order to inspect color concentration and water level of the bottle. Quadratic distance technique is applied for color concentration analysis based on a combination of Red, Green and Blue (RGB) histogram. The vertical and horizontal coordinates technique is used to inspect three conditions of the level, which are passed, overfill and underfill. The proposed system has achieved 100% accuracy using 246 samples