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Egg Defect Detection and Classification in Boiled Egg Industry with Surface Disturbance Removal on the Eggshell Based on Image Processing
(2025-01-01)
Chotchawalkul, Sasikan
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Chaipanya, Oraya
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Anuntachai, Anuntapat
In the boiled egg industry, quality inspection is typically conducted twice: before eggs are transported into the conveyor-based boiling system (before boiling), and after they exit the water-based cooling system prior to packaging (after cooling). These inspections are commonly carried out through human visual assessment, which demands substantial human resources and time. This paper presents an automated system for detecting and classifying defective eggs-such as those with cracks, dents, rough shells, and other surface anomalies-using image processing techniques. The system is designed to enhance the visibility of such defects while minimizing the impact of production-related surface disturbances, including water stains, reflections from the cooling process, and red stamps from imported eggs. The proposed system comprises two main approaches: (1) Defects Detection Method, which classifies eggs into two categories: intact and defective; and (2) Pixel Counting and Comparison Method, which classifies eggs into three categories: intact, cracked or dented, and exploded eggs. This system offers a practical and efficient solution for the egg processing industry, reducing reliance on human labor, minimizing inspection time, and lowering hardware requirements for industrial implementation.
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Skill Level Recognition in Writing for Elementary School Students in Thailand Using Image Processing Technology
(2024-01-01)
Jarusitratti, Nattwat
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Laohakul, Krittatee
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Anuntachai, Anuntapat
In the current context, the problem of developmental writing difficulties in children is of great importance for school-age children. Diagnosing whether a child has developmental writing difficulties requires the use of writing skills assessments. These assessments are used by professionals to evaluate and diagnose any abnormalities in a child's writing development. However, there are limitations in terms of format, as they often rely on expert physicians for diagnosis. This creates a significant need for human resources. To address this, we have designed a method for scoring based on writing skills assessments, utilizing image processing technology and criteria from existing standards. The scoring criteria include three aspects: article writing position, article format, and copying speed. For article writing position, we find the centroid of the text. Article format is assessed based on the aesthetics of the written article, which should form a parallelogram. Lastly, copying speed is determined by the number of lines visible on the paper, using pixel frequency analysis.
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System for Analysis and Verification of Exercise Postures with Equipment
(2024-01-01)
Kamcharoen, Chayanee
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Boriboon, Pragasit
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Anuntachai, Anuntapat
This paper is initiated in response to affectation from pandemic. The people demand to improve their heath by themselves, and the exercise equipment is easier to install at home. Exercising with equipment requires fundamental knowledge to avoid any injuries and reduce ineffective performance. The system analyzes exercise postures using equipment, encompassing 3 poses: Deadlift, Lat Pull Down, and Bench Press. Analysis is comparing the alignment of skeletal joints in the body from exercise videos through image processing and comparing correctness against expert's movement. Results and recommendations are displayed on a web application which is developed by the Django Framework. Test results indicate an improvement in users' exercise direction tendencies.
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Lung Cancer Prediction Model from Chest X-Ray Images
(2024-01-01)
Chaiyathed, Chayodom
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Thanesmaneekul, Ekawit
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Anuntachai, Anuntapat
Lung cancer is one of the leading causes of death globally. Early diagnosis of lung cancer is crucial for treatment and prognosis. Traditional medical techniques, such as chest x-rays, have limitations in the early diagnosis of lung cancer. This paper develops an image classification model for chest CT scans using deep learning with transfer learning techniques. The data is divided into three parts: a training set, a testing set, and a validation set. The development of this model can be applied to improve the efficiency of early lung cancer diagnosis, reduce the risk of human errors, and increase workflow efficiency in hospitals. In this paper, a model is developed to distinguish between normal images and images with lung cancer. This model can potentially assist physicians in accurately and rapidly diagnosing lung cancer.
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Weapon Detection in X-ray Image of Baggages
(2024-01-01)
Kundilokovit, Piyapat
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Thaweechoklertchaikul, Rimthaweep
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Anuntachai, Anuntapat
Due to the daily commutes of people by MRT trains, following the shooting incident at Paragon, the MRT system has implemented bag searches before entering the stations to look for concealed or hidden weapons. These searches are conducted manually, which sometimes may not be thorough enough and can take a significant amount of time. Especially during peak hours when many people are using the MRT, it is possible for some individuals to pass through the station without being searched. Such actions can render the security measures ineffective. Therefore, this paper proposes a study to find ways to address these issues. From the study and comparison of object detection processes for risky items, such as sharp objects or guns, in X-ray images of luggage, it was found that models such as CNN, RCNN, Detectron, RetinaNet, and Yolo achieved excellent results in object detection and recognition. The organizers plan to apply object detection techniques and improve the existing methods for detecting objects in X-ray images to be more efficient and accurate, capable of identifying a variety of risky items.

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