Min-Soo Park
Sungkyunkwan University · Engineering
About the Lab
Professor Min-Soo Park's research lab specializes in artificial intelligence and computer vision applications for environmental and societal safety, with a strong focus on wildfire detection, early warning systems, and disaster response. The lab develops deep learning and computer vision techniques to address real-world challenges such as data imbalance, small-object detection in complex scenes, and multi-label classification for intelligent monitoring. Key research directions include intelligent surveillance for construction site safety, heatwave impact prediction using big data, and media use behavior analysis using panel data models. The lab emphasizes practical, data-driven solutions that enhance public safety and environmental resilience.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15To minimize the damage caused by wildfires, a deep learning-based wildfire-detection technology that extracts features and patterns from surveillance camera images was developed. However, many studies related to wildfire-image classification based on deep learning have highlighted the problem of data imbalance between wildfire-image data and forest-image data. This data imbalance causes model performance degradation. In this study, wildfire images were generated using a cycle-consistent generati
Although there has been study on worker detection using computer vision (CV) for the safety of construction sites, it is still challenging to identify employees who are obstructed or have poor vision. To solve these problems, we propose a method of small and overlapping target (worker) detection at a complex construction site named SOC-YOLO. The method is based on YOLOv5 and utilizes distance intersection over union (DIoU) non-maximum suppression (NMS), incorporating weighted triplet attention,
Owing to abnormal climate phenomena worldwide, forests are becoming dry and heat waves have started to occur, increasing the damage caused by wildfires. In addition to causing significant human and material damage, wildfires are also a major cause of critical pollutant emissions, in which fine dust generated by incomplete combustion pollutes the atmosphere, soil, and water. Early detection and monitoring are some of the main ways for minimizing wildfire damage, and a topic of research interest i
Climate change increases the frequency and intensity of heatwaves, causing significant human and material losses every year. Big data, whose volumes are rapidly increasing, are expected to be used for preemptive responses. However, human cognitive abilities are limited, which can lead to ineffective decision making during disaster responses when artificial intelligence-based analysis models are not employed. Existing prediction models have limitations with regard to their validation, and most mo
In this article, we estimate the user substitutability and complementarity of media by using media diary data on the media use of individuals over the course of three days. Fixed-effects panel data models allow us to eliminate possible bias due to individual-specific media use propensity. We observe significant substitution among paper, television, and computer use, while telephone and computer use seem to be complementary in time of use. The magnitudes of substitutability and complementarity be
Given the explosive growth of information technology and the development of computer vision with convolutional neural networks, wildfire field data information systems are adopting automation and intelligence. However, some limitations remain in acquiring insights from data, such as the risk of overfitting caused by insufficient datasets. Moreover, most previous studies have only focused on detecting fires or smoke, whereas detecting persons and other objects of interest is equally crucial for w
INTRODUCTION: Fatal fall from height accidents, especially on construction sites, persist, underscoring the importance of monitoring and managing worker behaviors to enhance safety. Deep learning showed the possibility of substituting the manual work of safety managers. However, applying detection results to determine compliance with safety regulations has limitations. METHOD: This study estimated the actual working height depending on the height of the object detection bounding box by specifyin
Along with the fourth industrial revolution, the use of unmanned aerial vehicles (UAV) has grown very rapidly over the past decade. With this rapid growth, studies using UAVs are underway in various areas. UAVs are more economical and effective when utilizing several nodes rather than operating a single aircraft. In general, UAVs collect and transmit information to the control center (CC), and act on control commands from the CC. Communication between UAVs and the control center is usually achie
This paper investigates positively how the judges’ discretions are exercised in divorce decrees in relation with the judicial precedents and the doctrines and how much the freedom of divorce and the after-divorce welfare of a party of husband and wife are protected. In order to provide evidence, we implement an empirical or statistical analysis about the determination of ratio of marital property division and pain and suffering by using the first instance panel decrees rendered from 2009 to 2011
Research Areas
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