Boonsang, Siridech
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Preferred name
Boonsang, Siridech
Alternative Name
Boonsang, S.
Main Affiliation
Email
siridech.bo@kmitl.ac.th
2 results
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Item type:Publication, Deep Learning Based Visual Servo for Autonomous Aircraft Refueling(2025-03-01); This study develops and evaluates a deep learning based visual servoing (DLBVS) control system for guiding industrial robots during aircraft refueling, aiming to enhance operational efficiency and precision. The system employs a monocular camera mounted on the robot's end effector to capture images of target objects—the refueling nozzle and bottom loading adapter—eliminating the need for prior calibration and simplifying real-world implementation. Using deep learning, the system identifies feature points on these objects to estimate their pose estimation, providing essential data for precise manipulation. The proposed method integrates two-stage neural networks with the Efficient Perspective-n-Point (EPnP) principle to determine the orientation and rotation angles, while an approximation principle based on feature point errors calculates linear positions. The DLBVS system effectively commands the robot arm to approach and interact with the targets, demonstrating reliable performance even under positional deviations. Quantitative results show translational errors below 0.5 mm and rotational errors under 1.5° for both the nozzle and adapter, showcasing the system's capability for intricate refueling operations. This work contributes a practical, calibration-free solution for enhancing automation in aerospace applications. The videos and data sets from the research are publicly accessible at https://tinyurl.com/CiRAxDLBVS. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning(2024-11-01); Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting-edge automated tools are necessary to modernize disease screening using radiographs across various veterinary settings, such as animal clinics and hospitals. This is primarily due to the inherent challenges associated with interpreting uncertainties among veterinarians. In this study, an artificial intelligence model was developed to screen canine tracheal collapse using archived lateral cervicothoracic radiographs. This model can differentiate between a normal and collapsed trachea, ranging from early to severe degrees. The you-only-look-once (YOLO) models, including YOLO v3, YOLO v4, and YOLO v4 tiny, were used to train and test data sets under the in-house XXX platform. The results showed that the YOLO v4 tiny-416 model had satisfactory performance in screening among the normal trachea, grade 1–2 tracheal collapse, and grade 3–4 tracheal collapse with 98.30% sensitivity, 99.20% specificity, and 98.90% accuracy. The area under the curve of the precision–recall curve was >0.8, which demonstrated high diagnostic accuracy. The intraobserver agreement between deep learning and radiologists was κ = 0.975 (P <.001), with all observers having excellent agreement (κ = 1.00, P <.001). The intraclass correlation coefficient between observers was >0.90, which represented excellent consistency. Therefore, the deep learning model can be a useful and reliable method for effective screening and classification of the degree of tracheal collapse based on routine lateral cervicothoracic radiographs.