Fault Detection In Bottle Caps And Label Alignment Using Convolutional Neural Network

Quality control is one of the most crucial phases in the creation of any product. Product quality is the degree to which a product lives up to expectations. Due to flaws in bottle caps and label alignment, the quality of beverage products suffers. To overcome this problem, detection of fault via computer-based methods is a developing trend across many industries. In this paper by using Convolutional neural networks, image processing is used to detect fault in bottle caps which include loose caps, tilted caps, and missing caps and edge detection method is used to detect fault in label alignment which includes misplaced or tilted labels and inverted labels. The proposed model will improve precision and speed of fault detection. Due to the accuracy and time concession the adaptability of this machine learning method, the system can operate in a variety of lighting and backdrop conditions.