http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/355 2026-07-25T23:46:07Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1854 Image-based transient detection for GOTO sky survey Terry Cortez Natthakan Iam-on Thesis (M.Eng.) -- Computer Engineering, School of Information Technology. Mae Fah Luang University, 2022 2022-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1823 Image processing and deep learning for dairy cow monitoring Jaturon Niyarut Roungsan Chaisricharoen Livestock farming is a large-scale industry, particularly for beef and dairy cows, which generate economic value as primary food sources worldwide. Behavioral recognition of beef and dairy cows has traditionally required continuous human observation, which is subject to recording accuracy limitations and human fatigue. YOLOv8n was therefore applied to assist in tasks requiring continuous monitoring for the behavioral recognition of beef and dairy cows. Datasets were collected over 24 hours for model training, and the model was subsequently deployed on-farm to monitor a herd of 16 dairy cows for behavioral data collection over an additional 15 days to derive statistical metrics. The target behaviors were categorized into five classes: Eating, Walking, Lying, Standing, and Drinking, then the model was also applied to analyze the behavior of individual dairy cows during milking 44 times on during morning and evening. The total dataset comprised 12,848 images, partitioned in a train:val:test ratio of 70:15:15. The mAP50-95 metric was compared across five YOLO-based models. YOLOv8n achieved the highest performance owing to its 3.2M parameters, which exceed those of the other versions, yielding Precision 74.85%, Recall 78.74%, mAP@0.5 78.37%, and mAP50-95 49.18%. The classes with relatively lower performance were Walking and Drinking, as their image counts were fewer than those of the other classes because the dairy cow within the farm had limited space for walking. The research primarily focused on the Eating class, as it is instrumental in predicting whether a herd of dairy cow in a farm is at risk of illness. Thesis (M.Eng.) -- Computer Engineering, School of Applied Digital Technology. Mae Fah Luang University, 2025 2025-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1687 Indoor scene classification using machine learning on object-detection based features Simon Yosboon Khwunta Kirimasthong The classification of scenes from images is a fundamental task in computer vision, vital for various applications ranging from autonomous driving to surveillance systems. An ongoing challenge in this field is the identification of discriminative features for accurate classification. This study addresses this challenge by comparing the effectiveness of two approaches: object-based feature extraction and deep learning. We propose a novel methodology that leverages YOLOv3, a state-of-the-art pre-trained model for object detection, to extract object-based features from scene images. By utilizing YOLOv3, we obtain feature vectors representing the presence and characteristics of objects within each scene. These features are then used as input for four distinct machine learning algorithms to classify scenes. Concurrently, we develop a deep learning model using the original images, which typically requires more computational resources and time for training. We conduct comprehensive experiments to evaluate the performance of both approaches across various scene classification tasks. Surprisingly, our results demonstrate that simple machine learning models utilizing object-level features achieve comparable performance to deep learning methods. This finding suggests that focusing on object-based representations can effectively classify scenes while circumventing the resource-intensive nature of deep learning algorithms. Thesis (M.Eng.) -- Computer Engineering, School of Information Technology. Mae Fah Luang University, 2024 2024-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1683 Extra trees model with minority target oversampling for classification of dementia and heart failure in adults Pornthep Phanbua Punnarumol Temdee Thesis (M.Eng.) -- Computer Engineering, School of Applied Digital Technology. Mae Fah Luang University, 2024 2024-01-01T00:00:00Z