Nutchanon Suvittawat

Airport turnaround is an important operational process that directly affects flight punctuality, airport capacity, and ground-handling efficiency. However, many turnaround activities are still monitored manually or through fragmented operational records, which can limit real-time visibility and delay identification. This study proposes a computer vision-based airport turnaround monitoring pipeline that integrates YOLOv11 object detection, Norfair multi-object tracking, and frame differencing-based motion analysis to extract key operational events from airport video footage. Publicly available turnaround footage from Shinshu Matsumoto Airport, Japan, was collected under different environmental conditions, including daytime, nighttime, rainy, after-rain, and transition lighting conditions. From selected videos, 1446 images were labeled into 11 airport turnaround object classes, including tow tug, aerobridge, airplane, baggage container, belt loader, belt loader roof, fuel line, fuel tanker, fuel tube, tractor, and window. The dataset was divided into training, validation, and testing sets using a 70:20:10 ratio. The trained YOLOv11 model achieved strong detection performance, with overall test an precision of 0.9609, recall of 0.9445, and mAP50 of 0.9617. To support activity-level interpretation beyond object detection, the proposed pipeline applies frame differencing within specific regions of interest, including the aerobridge window region for passenger deboarding and boarding detection, and the belt loader roof region for baggage unloading and loading detection. The extracted object detections, motion spikes, and temporal logs are then converted into a Gantt chart that summarizes major turnaround activities, including airplane parking, deboarding, baggage unloading, refueling, baggage loading, boarding, and pushback. The results demonstrate that the proposed modified YOLO-based pipeline can transform ordinary airport video footage into structured operational timelines, supporting more transparent, data-driven, and automated monitoring of airport turnaround processes.

Electric vehicles (EVs) are increasingly recognized as a sustainable transportation solution; however, mass-market adoption in Thailand remains limited due to infrastructure constraints, technological complexity, and evolving consumer perceptions. This study examines the effects of charging infrastructure accessibility, perceived ease of use, and psychological driving experience on consumers’ willingness to pay (WTP) for EVs. A quantitative approach was employed using survey data collected from 400 EV users and analyzed through Structural Equation Modeling (SEM). Grounded in the Technology Acceptance Model (TAM) and the Theory of Consumption Values (TCV), the study reveals that charging infrastructure accessibility significantly enhances perceived ease of use, driving experience, and WTP. In addition, perceived ease of use and driving experience positively influence consumers’ financial commitment toward EV adoption and partially mediate the relationship between infrastructure accessibility and WTP. The findings indicate that EV consumer valuation is shaped by both functional infrastructure readiness and psychological user experience. The study contributes to EV consumer behavior literature by integrating cognitive and experiential perspectives and provides practical implications for policymakers and industry stakeholders seeking to accelerate EV adoption in Thailand.

The global shift toward electric vehicles (EVs) has accelerated as governments pursue low-carbon transport systems and sustainable mobility transitions. In emerging economies such as Thailand, however, consumer adoption remains influenced by a complex interplay of policy incentives, perceived benefits, and charging-related uncertainties. This study investigates the determinants of EV adoption intention by integrating ecosystem and policy support with perceived value and perceived risk within a unified analytical framework. Grounded in customer perception theory and technology adoption perspectives, this research addresses the fragmented treatment of these factors in prior studies. Data were collected from 400 respondents with prior EV experience and analyzed using structural equation modeling to examine both direct and mediated relationships. The findings reveal that ecosystem and policy support significantly strengthen adoption intention, primarily by enhancing perceived value and reducing perceived risk. These results highlight the pivotal role of perception-based mechanisms in translating policy initiatives into consumer commitment. The study suggests that effective EV promotion in Thailand and similar emerging markets requires coordinated ecosystem development, clear policy communication, and reliable charging infrastructure to sustain long-term adoption momentum.

Maritime surveillance is critical for ensuring the safety and continuity of sea logistics, port operations, and coastal activities in the presence of anomalies such as unlawful maritime activities, security-related incidents, and anomalous events (e.g., tsunamis or aggressive marine wildlife). Recent advances in unmanned aerial vehicles (UAVs)/drones and computer vision enable automated, wide-area monitoring that can reduce dependence on continuous human observation and mitigate the limitations of traditional methods in complex maritime environments (e.g., waves, ship clutter, and marine animal movement). This study proposes a hybrid anomaly detection and tracking pipeline that integrates YOLOv12, as the primary object detector, with two auxiliary modules: (i) motion assistance for tracking moving anomalies and (ii) stillness (appearance) assistance for tracking slow-moving or stationary anomalies. The system is trained and evaluated on a custom maritime dataset captured using a DJI Mini 2 drone operating around a port area near Bayshore MRT Station (TE29), Singapore. Windsurfers are used as proxy (dummy) anomalies because real anomaly footage is restricted for security reasons. On the held-out test set, the trained model achieves over 90% on Precision, Recall, and mAP50 across all classes. When deployed on real maritime video sequences, the pipeline attains a mean Precision of 92.89% (SD 13.31), a mean Recall of 90.44% (SD 15.24), and a mean Accuracy of 98.50% (SD 2.00%), indicating strong potential for real-world maritime anomaly detection. This proof of concept provides a basis for future deployment and retraining on genuine anomaly footage obtained from relevant authorities to further enhance operational readiness for maritime and coastal security.

To address the limitations of traditional aircraft skin inspection, the aviation industry and academia have increasingly been exploring the integration of computer vision technologies into the defect detection process. These implementations of computer vision technologies rely on the performance of underlying neural network models, whose effectiveness is highly influenced by their hyperparameter configuration. To obtain optimum hyperparameters, an optimization procedure is often employed to optimize a certain combination of the model’s performance metrics. However, in the aircraft skin defect detection domain, studies to inspect the effect of different emphases in the performance metrics considered in this objective function are still not widely available. In this paper, we present our early observations regarding the influence of different performance metrics’ emphases during the hyperparameter tuning process on the overall performance of a computer vision model employed for aircraft skin defect detection. In this preliminary inspection, we consider the utilization of YOLOv12 and the Bayesian Optimization approach for the defect detection model and hyperparameter optimizer, respectively. We highlight the possible performance degradation of the model after a hyperparameter tuning procedure when the weight factor distribution of the performance metrics is not carefully considered. We note several weight factors of interest that could serve as initial possible “safe spots” for further exploration.

This study investigates consumer behavior in electric vehicle (EV) adoption, focusing on how factors like convenience, accessibility, technological perception, and cost influence the travel patterns and usage behavior of EV drivers in Thailand. This study aims to address the research gap in the comparative behavior between electric vehicles and public transport in a developing country. Using a quantitative approach, the study collected data via surveys distributed online and face-to-face interviews with a stratified sample of 398 respondents. The survey assessed the relationships between convenience and accessibility, technology perception, cost of ownership, and travel patterns using structural equation modeling (SEM). The findings reveal that convenience and accessibility significantly affect consumer perceptions of technology and the cost of ownership, which, in turn, influences travel patterns. Technology perception and performance serve as partial mediators, suggesting that improving the infrastructure enhances EV adoption. Additionally, the cost of ownership, including long-term savings, positively impacts usage behavior. This study provides key insights for policymakers and urban planners aiming to promote the adoption of EVs. Enhancing charging infrastructure, offering government incentives, and improving public awareness of long-term cost benefits are recommended strategies. These findings are particularly relevant in urban environments and offer guidance for developing infrastructure policies that align with consumer preferences.

Aircraft skin surface defect detection is critical for aviation safety but is currently mostly reliant on manual or visual inspections. Recent advancements in computer vision offer opportunities for automation. This paper reviews the current state of computer vision algorithms and their application in aircraft defect detection, synthesizing insights from academic research (21 publications) and industry projects (18 initiatives). Beyond a detailed review, we experimentally evaluate the accuracy and feasibility of existing low-cost, easily deployable hardware (drone) and software solutions (computer vision algorithms). Specifically, real-world data were collected from an abandoned aircraft with visible defects using a drone to capture video footage, which was then processed with state-of-the-art computer vision models—YOLOv9 and RT-DETR. Both models achieved mAP50 scores of 0.70–0.75, with YOLOv9 demonstrating slightly better accuracy and inference speed, while RT-DETR exhibited faster training convergence. Additionally, a comparison between YOLOv5 and YOLOv9 revealed a 10% improvement in mAP50, highlighting the rapid advancements in computer vision in recent years. Lastly, we identify and discuss various alternative hardware solutions for data collection—in addition to drones, these include robotic platforms, climbing robots, and smart hangars—and discuss key challenges for their deployment, such as regulatory constraints, human–robot integration, and weather resilience. The fundamental contribution of this paper is to underscore the potential of computer vision for aircraft skin defect detection while emphasizing that further research is still required to address existing limitations.

Aircraft skin surface defect detection is critical for aviation safety but is currently mostly reliant on manual or visual inspections. Recent advancements in computer vision offer opportunities for automation. This paper reviews the current state of computer vision algorithms and their application in aircraft defect detection, synthesizing insights from academic research (21 publications) and industry projects (18 initiatives). Beyond a detailed review, we experimentally evaluate the accuracy and feasibility of existing low-cost, easily deployable hardware (drone) and software solutions (computer vision algorithms). Specifically, real-world data were collected from an abandoned aircraft with visible defects using a drone to capture video footage, which was then processed with state-of-the-art computer vision models — YOLOv9 and RT-DETR. Both models achieved mAP50 scores of 0.70–0.75, with YOLOv9 demonstrating slightly better accuracy and inference speed, while RT-DETR exhibited faster training convergence. Additionally, a comparison between YOLOv5 and YOLOv9 revealed a 10% improvement in mAP50, highlighting the rapid advancements in computer vision in recent years. Lastly, we identify and discuss various alternative hardware solutions for data collection — in addition to drones, these include robotic platforms, climbing robots, and smart hangars — and discuss key challenges for their deployment, such as regulatory constraints, human-robot integration, and weather resilience. The fundamental contribution of this paper is to underscore the potential of computer vision for aircraft skin defect detection while emphasizing that further research is still required to address existing limitations.

The growing adoption of electric vehicles (EVs) necessitates a well-distributed network of charging stations. However, selecting optimal locations for these stations is a complex issue influenced by geographic, demographic, technical, and economic factors. This study aims to fill the gaps in previous research by providing a comprehensive analysis of factors influencing the selection of EV battery charging stations. This research focuses on integrating geographic, demographic, technical, and infrastructure considerations to inform strategic placement decisions. A quantitative approach was employed, using questionnaires distributed to 300 entrepreneurs in Thailand’s EV charging station sector. The data were analyzed using descriptive statistics and structural equation modeling (SEM) to evaluate the relationships among the influencing factors. The results reveal that technical and infrastructure factors significantly impact economic and financial considerations, which in turn influence the selection of charging stations. Additionally, geographic and demographic factors play a crucial role in shaping economic outcomes and the strategic placement of these stations. A holistic approach that integrates these diverse factors is essential for the strategic deployment of EV charging infrastructure, which supports increased EV adoption and contributes to environmental sustainability.

This study examines the determinants influencing clean food purchasing decisions among consumers in the lower northeastern region of Thailand. It addresses the significant research gap in understanding the interplay between product attributes, cultural background, and healthy eating behaviors. Using the Theory of Planned Behavior, this study employs a questionnaire survey with 392 valid responses, analyzed through structural equation modeling. Results indicate that both product attributes and cultural background significantly impact healthy eating behaviors, which, in turn, mediate their effects on clean food purchasing decisions. Specifically, healthy eating behaviors were found to partially mediate the relationship between product attributes and purchasing decisions, as well as between cultural background and purchasing decisions. The findings underscore the importance of promoting product attributes and cultural awareness to enhance healthy eating behaviors, thereby encouraging clean food purchases. This research provides valuable insights for policymakers and marketers aiming to foster healthier eating habits through targeted interventions and culturally sensitive marketing strategies.