Hanskunatai, Anantaporn

Loading...
Thumbnail Image
Preferred name
Hanskunatai, Anantaporn
Alternative Name
Hanskunatai, Anantapom
Main Affiliation
Email
anantaporn.ha@kmitl.ac.th

Settings

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Inspection System for Glass Bottle Defect Classification based on Deep Neural Network
    (2023-01-01)
    Claypo, Niphat
    ;
    Jaiyen, Saichon
    ;
    Hanskunatai, Anantaporn
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    FAST DEFECT DETECTION FOR GLASS BOTTLE USING AUTOENCODER AND ERROR THRESHOLD
    (2024-06-01)
    Hanskunatai, Anantaporn
    ;
    Jaiyen, Saichon
    ;
    Claypo, Niphat
    Glass bottle defect detection is an important part of quality control process in any glass manufacturing industry. The bottles must be inspected before packaging. Machine vision for glass bottle defect detection is the technology and method to inspect and analyze the defects for images automatically. Machine vision requires high-ability method to detect the defect and reject the bottle with the defect quickly. In this paper, defect detection framework for glass bottle defect detection tasks using autoencoders and error threshold is proposed. The fast detection method, a small autoencoder neural network architecture was designed with only good bottle images to train an autoencoder neural network. The decoded images are representations of normal bottle images and calculate threshold errors value. Defect detection is done by comparing the error between the normal background image and the encoded images to a threshold error from the training set. The performance of our method was compared to several other methods: VGG16, MobileNetV3, ADA, edge detection and image threshold. The experimental results show that our method yields 80% of accuracy on the body dataset and 92% of accuracy on the neck dataset. The average training time of our method is faster than that of all other neural network-based methods. From the experimental results, we can conclude that our defect detection framework outperforms other approaches both in accuracy and training time for defect detection on the side wall of a glass bottle.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    FACE SPOOFING DETECTION BASED ON DEEP FEATURE EXTRACTION AND INSTANCE-BASED CLASSIFICATION
    (2023-02-01)
    Claypo, Niphat
    ;
    Jaiyen, Saichon
    ;
    Hanskunatai, Anantaporn
    Face recognition is an important task in smart home security for detecting a face or monitoring a person in a live video and verifying the identity of an authentic user. However, there have been spoofing face methods that can trick a face recognition algorithm into wrongly verifying the identity of the person. In this paper, we propose a new hybrid framework for spoofing face detection based on Convolutional Neural Network and Long Short-Term Memory (CNNLSTM) and instance-based learning algorithm. In addition, a new dataset called FSA-CCTV is proposed, which contains face images from CCTV video clips with many types of spoofing attacks. The performance of our method was compared to several other anti-spoofing methods: CNN and RI-LBP, SLRNN, HSV+YCbCr, ResNet50, YCbCr+SVM and YCbCr+KNN. The experimental results show that our method yielded 93.2% of Accuracy, 96.8% of Recall, 94% of Precision, 94.8% of F<inf>1</inf>-score and 0.93 of AUC on the FSA-CCTV dataset. From the experimental results we can conclude that the proposed algorithm outperforms other approaches and yielded the most stable classification accuracy on the proposed dataset.