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Engineering educational research section Mechanical and Intelligent Engineering Program |
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KHIN DAGON WIN
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Degree 【 display / non-display 】
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Doctor of Philosophy ( 2021.9 University of Miyazaki )
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Master of Engineering ( 2017.9 University of Miyazaki )
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Master of Computer Science ( 2018.8 University Of Computer Studies, Mandalay )
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Bachelor of Computer Science ( 2014.2 University Of Computer Studies, Mandalay )
Research Areas 【 display / non-display 】
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Informatics / Intelligent informatics / Agricultural engineering, Computer vision, Image processing, Medical Engineering, Cloud computing
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Informatics / Robotics and intelligent system
Papers 【 display / non-display 】
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Khin Dagon Win, Kikuhito Kawasue, Masatoki Kaneko
Sensors 25 ( 6 ) 2025.3
Authorship：Lead author Publishing type：Research paper (scientific journal)
Highlights: What are the main findings? A non-contact method for measuring neonatal head circumference using a 3D imaging sensor was developed. What is the implication of the main finding? A top-view 3D imaging system can be used to estimate the head circumferences of neonates without direct contact, ensuring safety for their delicate skin. Because some portions of the head may be missing in the top-view images, these missing areas are estimated using mat information. The extracted features and head surface values contribute to the head circumference measurement. This approach enhances the feasibility of non-contact head circumference measurement, which could improve neonatal monitoring in clinical settings. In Japan, birth rates are declining, but there are a rising number of underweight newborns who require specialized care in neonatal intensive care units (NICUs). Head circumference is an important indicator of brain development for low-birth-weight infants. However, measuring head circumference requires extreme care because low-birth-weight infants have fragile skin. Therefore, a non-contact measurement system using a 3D imaging sensor was developed. Using this system, three-dimensional data for a newborn’s head can be obtained from outside the incubator. Briefly, the images are taken from above the incubator, so there is an area behind the head that cannot be captured by the camera, but the head circumference estimation takes into account the fact that the head is in contact with the mat. The proposed method allows head circumference estimation without touching the newborn. This approach minimizes stress for both the neonate and the nurse and improves efficiency and safety in the NICU.
DOI： 10.3390/s25061869
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Robust pig extraction using ground base depth images for automatic weight estimation Reviewed
Khin Dagon Win, Kikuhito Kawasue, Tadaaki Tokunaga
Artificial Life and Robotics 30 ( 1 ) 42 - 50 2025.2
Authorship：Lead author Publishing type：Research paper (scientific journal)
Dark colored pigs (Berkshire, Duroc, etc.) are widely recognized nationwide in Japan for their exceptional taste, with the southern Kyushu region being a renowned production area for these esteemed breeds. However, estimating the weight of these pigs using a camera presents a unique challenge. The key process in a camera-based weight estimation system is the precise extraction of the target pig from the background. Typically, cameras capture images from above, as the top-view images provide the most specific growth indicators. However, the image from above contains a ground image. Since Berkshire and Duroc pigs are black and red, respectively, they blend into the ground, making it difficult to accurately segment the pigs in the images. Thus, it is crucial to perfectly distinguish between the ground and the pigs. Therefore, a new extraction method is proposed to distinguish between the ground and pigs by converting depth data based on the pig's position. To enhance the efficiency of pig farming and alleviate the burden on workers, our goal is to develop a system that automatically measures the weight of Berkshire pigs for shipment without background interference. In this study, we installed the system at a Berkshire pig farm and demonstrated the effectiveness of this innovative extraction method for camera-based weight estimation.
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Handheld Pig-Weight Estimation Using an RGB-D Sensor Reviewed
Khin Dagon Win, Kikuhito Kawasue, Tadaaki Tokunaga, Yusuke Iki
Lecture Notes in Electrical Engineering 1322 LNEE 95 - 103 2025
Authorship：Lead author Publishing type：Research paper (international conference proceedings)
Although mechanical weighing machines specialized for pigs are normally used to measure animal weights on pig farms, guiding those animals onto the weighing machines is a difficult and unpleasant chore that normally takes 20 seconds to complete after the pig is positioned on the load cell. Additionally, pig weighing machines are prone to mechanical breakdowns and are unpleasant to repair because of the farm residue they collect. Therefore, the development of a more practical and robust weight measurement apparatus is desired. In the present paper, we report on a handheld weight estimation device using a red-green-blue-depth (RGB-D) sensor with a laser slit system. An RGB-D sensor system is used to estimate pig weights, and the laser slit is used to align the measurement direction of the RGB-D sensor along the pig’s body. In operation, this system captures a depth image of the pig under examination and then uses it to create three-dimensional (3D) data. The 3D body parameters are extracted using image processing, and those extracted 3D body parameters for the pig are then fed into a random forest algorithm to produce the weight estimation. We also report on experimental results that demonstrate the reliability of our pig weight estimation system and its suitability for practical use.
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Kikuhito Kawasue, Khin Dagon Win, Kumiko Yoshida, Geunho Lee
Artificial Life and Robotics 29 ( 1 ) 37 - 42 2024.2
Publishing type：Research paper (scientific journal)
In pig production, the number of pigs raised on each farm is increasing, but the population of workers involved in pig production is decreasing, so lighter labor is expected. On the other hand, it is also important to improve pig grading and profitability. Weight is a major criterion for pig grading. Too heavy or too light will decrease profits, and pigs need to be shipped at the appropriate weight. However, since each pig weighs more than 100 kg, weighing each pig is very labor-intensive. In large farms, more than 50 pigs are kept in a single piggery, and they are shipped together at the same time, after determining the day when they have reached the proper weight for shipment. In order to improve profitability, it is important to control the growth of pigs in a piggery so that they grow uniformly and to determine the appropriate shipping date. In this study, a prototype system was developed to automatically measure daily weight distribution. If the weight distribution in the piggery is known, appropriate shipping dates can be determined. This paper reports the results of a valid experiment using the developed system.
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Kikuhito Kawasue, Pwint Phoo Wai, Khin Dagon Win, Geunho Lee, Yusuke Iki
Artificial Life and Robotics 28 ( 1 ) 89 - 95 2023.2
Publishing type：Research paper (scientific journal)
Pig weights are important indicator for the healthcare and the economic operation of pig farms, and the development of a system to easily estimate these weights is desired. Although load cells are usually used for actual measurement in pig farms, it is not easy to guide pigs weighing more than 100 kg to the scales because many pigs do not like to get on the scales. Therefore, a convenient pig weight estimation system using RGB-D sensors has been developed. An RGB-D sensor (Intel Realsense D455) is used as the sensing device for weight estimation. Weight estimation is performed on 3D point cloud data of photographed pig images. When capturing pigs, it is desirable to have a constant camera orientation toward the pigs However, it is not easy to always capture from the same direction because pigs move around quickly in the piggery. A method with a high degree of freedom in the capture direction by exploiting pig symmetry of the pig’s body is introduced in this paper. The system is applied for a wearing device using AR (Augmented Reality) glasses. Experimental results show the feasibility of this system.
MISC 【 display / non-display 】
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バークシャー種肥育豚における3Dカメラを用いた体重推定 Reviewed
泊鞠沙, 仲間ひなた, 浦川啓介, 川末紀功仁, WIN Khin Dagon, 徳永忠昭
日本暖地畜産学会報 67 ( 2 ) 2024
Publishing type：Rapid communication, short report, research note, etc. (scientific journal)
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Pig Weight Estimation Using Machine Learning Reviewed
金澤波音, 川末紀功仁, KHIN DAGON Win, 壱岐侑祐
計測自動制御学会システムインテグレーション部門講演会(CD-ROM) 22nd 2021
Publishing type：Rapid communication, short report, research note, etc. (scientific journal)
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カメラを用いたオートソーティング装置による肥育豚省力出荷体系 Reviewed
岐本博紀, 岩切正芳, 壱岐侑祐, 三角久志, 加地雅也, 島村勝則, 松窪敬介, 吉田久美子, 冷水忠, 日高良太, 合志文利, 川島知之, 川末紀功仁, WIN Khin Dagon
九州沖縄農業試験研究の成果情報(Web) 2020 2020
Publishing type：Rapid communication, short report, research note, etc. (scientific journal)
Presentations 【 display / non-display 】
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Robust pig extraction using ground base depth images for automatic weight estimation International conference
Khin Dagon Win, Kikuhito Kawasue, Tadaaki Tokunaga & Yusuke Iki
AROB 30th 2025 2025.1.24
Event date： 2025.1.22 - 2025.1.24
Presentation type：Oral presentation (general)
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Handheld Pig-Weight Estimation Using an RGB-D Sensor International conference
Khin Dagon Win, Kikuhito Kawasue, Tadaaki Tokunaga & Yusuke Iki
ICGEC2024 2024.8.28
Event date： 2024.8.28 - 2024.8.29
Presentation type：Oral presentation (general)