Seunghyeon Wang 교수

Inspecting the number of rebars in each column of a reinforced concrete (RC) structure is a significant task that must be undertaken during the rebar inspection process. Conventionally, counting the rebars has relied on a manual inspection carried out by visiting inspectors. However, this approach is very time-consuming, labor-intensive, and poses a potential safety risk. Previous studies have focused on the applications of counting the rebars for a production line and/or warehouse, using vision

• Nine distinct image augmentation techniques—Brightness, Contrast, Perspective, Rotate, Scale, Shear, Translate, Blurring, and a combined application termed "A Sum of Techniques"—were applied to two tasks: window detection and window state detection. • These techniques were demonstrated using three deep learning-based object detection methods: Faster R-CNN, YOLO, and R-FCN, with five widely-used architectures: ResNet-50, ResNet-101, MobileNetV3, EfficientNetV2, and InceptionV3. • The results hi

Monitoring heavy equipment in real time is crucial for ensuring safety and operational efficiency at construction sites, yet achieving both high detection accuracy and fast inference remains challenging under diverse environmental conditions. Although previous studies have attempted to improve accuracy and speed, their findings often lack generalizability, partly due to inconsistent datasets and the need for more advanced techniques. In response, this study proposes an enhanced object detection

Non-Protective Personal Equipment (PPE) detection is crucial on construction sites. Although deep learning models are adept at identifying such information from on-site cameras, their success relies on large, diverse, and high-quality datasets. Image augmentation offers an alternative for artificially broadening dataset diversity. However, its impact on non-PPE detection in construction environments has not been adequately examined. This study introduces a methodology applying eight distinct aug

Ensuring proper Personal Protective Equipment (PPE) compliance is crucial for maintaining worker safety and reducing accident risks on construction sites. Previous research has explored various object detection methodologies for automated monitoring of non-PPE compliance; however, achieving higher accuracy and computational efficiency remains critical for practical real-time applications. Addressing this challenge, the current study presents an extensive evaluation of You Only Look Once version

Accurate inspection of rebars in Reinforced Concrete (RC) structures is essential and requires careful counting. Deep learning algorithms utilizing object detection can facilitate this process through Unmanned Aerial Vehicle (UAV) imagery. However, their effectiveness depends on the availability of large, diverse, and well-labelled datasets. This article details the creation of a dataset specifically for counting rebars using deep learning-based object detection methods. The dataset comprises 87

Accurate inspection of Reinforced Concrete (RC) structures requires precise rebar counting. Although deep-learning object detectors can extract this information from drone imagery, their effectiveness depends on large, diverse, and well-labeled datasets. Image augmentation can increase data variability, yet its impact on Unmanned Aerial Vehicles (UAVs)-based rebar counting has been underexplored. This study systematically evaluates ten augmentation methods-brightness, contrast, perspective, rota

There are numerous applications for building dimension data, including building performance simulation and urban heat island investigations. In this context, object detection and instance segmentation methods—based on deep learning—are often used with Street View Images (SVIs) to estimate building dimensions. However, these methods typically depend on large and diverse datasets. Image augmentation can artificially boost dataset diversity, yet its role in building dimension estimation from SVIs r

Fault detection and diagnosis (FDD) in Air Handling Units (AHUs) ensure building functions such as energy efficiency and occupant comfort by quickly identifying and diagnosing faults. Combining deep learning with FDD has demonstrated high generalization ability in this field. To develop deep learning models, this research constructed a dataset sourced from real data collected from a large-scale office in South Korea. The raw AHU data were extracted from the Building Management System (BMS) at 1-

Automated monitoring of safety helmets at construction sites is essential for injury prevention and ensuring safety compliance. Although object detection methods have been extensively used in prior research, direct comparisons remain challenging due to data set variations, many of which are not publicly accessible. Additionally, enhancing detection accuracy and computational efficiency remains necessary for practical real-time monitoring. To overcome these limitations, this study evaluates you o

Static Street View Images (SSVIs) are widely used in urban studies to analyze building characteristics. Typically, camera parameters such as pitch and heading need precise adjustments to clearly capture these features. However, system errors during image acquisition frequently result in unusable images. Although manual filtering is commonly utilized to address this problem, it is labor-intensive and inefficient, and automated solutions have not been thoroughly investigated. This research introdu

Defect classification from text descriptions written by inspectors during the construction stage can be highly beneficial, offering advantages such as cost savings and improved reputation of apartment complexes by allowing early identification and resolution of issues. Combining automated methods with textual data can facilitate the rapid identification and diagnosis of faults. To develop such automated methods, this research constructed a dataset from real-world data collected from three apartm