Inspection System for Glass Bottle Defect Classification based on Deep Neural Network

The problem of defects in glass bottles is a significant issue in glass bottle manufacturing. There are various types of defects that can occur, including cracks, scratches, and blisters. Detecting these defects is crucial for ensuring the quality of glass bottle production. The inspection system must be able to accurately detect and automatically determine that the defects in a bottle affect its appearance and functionality. Defective bottles must be identified and removed from the production line to maintain product quality. This paper proposed glass bottle defect classification using Convolutional Neural Network with Long Short-Term Memory (CNNLSTM) and instant base classification. CNNLSTM is used for feature extraction to create a representation of the class data. The instant base classification predicts anomalies based on the similarity of representations of class data. The convolutional layer of the CNNLSTM method incorporates a transfer learning algorithm, using pre-trained models such as ResNet50, AlexNet, MobileNetV3, and VGG16. In this experiment, the results were compared with ResNet50, AlexNet, MobileNetV3, VGG16, ADA, Image threshold, and Edge detection methods. The experimental results demonstrate the effectiveness of the proposed method, achieving high classification accuracies of 77% on the body dataset, 95% on the neck dataset, and an impressive 98% on the rotating dataset.