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FAST DEFECT DETECTION FOR GLASS BOTTLE USING AUTOENCODER AND ERROR THRESHOLD
(2024-06-01)
Hanskunatai, Anantaporn
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Jaiyen, Saichon
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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.
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FACE SPOOFING DETECTION BASED ON DEEP FEATURE EXTRACTION AND INSTANCE-BASED CLASSIFICATION
(2023-02-01)
Claypo, Niphat
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Jaiyen, Saichon
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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.
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Inspection System for Glass Bottle Defect Classification based on Deep Neural Network
(2023-01-01)
Claypo, Niphat
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Jaiyen, Saichon
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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.
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A new streaming learning for stream chunk data classification based on incremental learning and adaptive boosting algorithm
(2018-01-01)
Claypo, Niphat
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Hanskunatai, Anantaporn
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Jaiyen, Saichon
Currently, stream data classification is a challenge task to discover new useful knowledge from massive and dynamic data in big data era. This paper proposes a streaming learning method based on the incremental learning using a new adaptive boosting algorithm for stream data. The proposed adaptive boosting consists of a new method for updating distribution weight and the new weight voting. This learning method concentrates on learning from sequential chunks of data stream. The distribution weight updating method uses error of previous hypothesis to update the weight. The learning method uses only one data chunk to create a new hypothesis at a time and after learning, the learned data chunk can be thrown away and can learn the new data chunk without using the previous learned data. The experimental results show that the accuracy of the proposed method is higher than other methods in all datasets.
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Automatic keyword selection for sentiment analysis using class dependency and dissimilarity
(2016-11-18)
Claypo, Niphat
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Jaiyen, Saichon
Sentiment analysis is a task for analyzing and extracting opinions from review documents on web sites, blogs, social media, and others in order to understand the opinions of consumers. Sentiment analysis methods can analyze sentiments of people and identify types of sentiment by classifying them into positive or negative opinions. In this paper, we propose a new automatic keyword selection method for selecting subsets of keywords for sentiment analysis using the information of class dependency and dissimilarity. The proposed method can be used for removing noisy words for reducing the size of training data for faster training by classifiers. The experimental results show that the proposed method can select the concerned subset of keywords for reducing the size of training data and can improve the classification performance of classifiers, compared with four different types of classifiers.
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A new feature selection based on class dependency and feature dissimilarity
(2015-11-20)
Claypo, Niphat
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Jaiyen, Saichon
Feature selection method is an important task for data preprocessing in data mining. Before a classifier learns the training data, there are a lot of features in each data set that makes the learning process slower. It is not appropriated for big data analytics. This paper proposes feature selection method based on the class dependency and feature dissimilarity (CDFD) using mutual information and Euclidean distance. The mutual information is applied to determine the dependency between the feature and the class if the dataset contains discrete data. If the dataset contains continuous data, the correlation between the feature and the class is used instead. The Euclidean distance is used for reducing the duplicated features based on dissimilarity between features. The experiments are conducted on five datasets. From the experimental results, the propose feature selection method can reduce the number of features in the data set and reduce the classification error of classifiers. Furthermore, it can be applied to discrete and continuous data and it can help classifiers improving their classification accuracies and reducing the computational times for learning.
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Opinion mining for thai restaurant reviews using K-Means clustering and MRF feature selection
(2015-02-27)
Claypo, Niphat
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Jaiyen, Saichon
Opinion mining on millions of Thai restaurant reviews in an unsupervised manner is a challenging task to survey feedbacks of the customers on their products and services. This is extremely helpful for owners to improve their business. In this paper, we propose an opinion mining on Thai restaurant reviews using K-Means clustering and MRF feature selection. The proposed method begins with text preprocessing for breaking reviews into words and removing stop words, followed by text transformation for creating keywords and generating input vectors. MRF feature selection is subsequently adopted for selecting relevant features from a large number of features extracted. Then, K-Means is employed for clustering into positive and negative reviews. From the experimental results, MRF feature selection can efficiently reduce the number of features in the data set so the computational time is significantly decreased. In addition, K-means can achieve the best clustering performance, when compared with Self-Organizing Map, Fuzzy C-Means, and Hierarchical Clustering. Thus, the cooperation of K-means with MRF feature selection is an effective model for clustering Thai restaurant reviews.
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Opinion mining for Thai restaurant reviews using neural networks and mRMR feature selection
(2014-01-01)
Claypo, Niphat
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Jaiyen, Saichon
Currently, Thai restaurants are popular around the world. There are tons of reviews related to foods and services in social networking websites. These tons of customer reviews make it difficult to analyze the opinions of customer toward foods and services. To help the businesses, the model of opinion mining is proposed for classifying the reviews and to analyze the attitude of customers for improving their products and services. In this research, the artificial neural network is applied to classify the positive and negative reviews. In addition, the mRMR feature selection is used to select the features of data in order to reduce the number of features in the data set. Consequently, the computational times of learning algorithms for neural networks are reduced. The experimental results show that the neural network is an effective model for classifying the Thai restaurant reviews.

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