PURPOSES : This study aimed to compare the object detection performance based on various analysis methods using point-cloud data collected from LiDAR sensors with the goal of contributing to safer road environments. The findings of this study provide essential information that enables automated vehicles to accurately perceive their surroundings and effectively avoid potential hazards. Furthermore, they serve as a foundation for LiDAR sensor application to traffic monitoring, thereby enabling the collection and analysis of real-time traffic data in road environments. METHODS : Object detection was performed using models based on different point-cloud processing methods using the KITTI dataset, which consists of real-world driving environment data. The models included PointPillars for the voxel-based approach, PartA2-Net for the point-based approach, and PV-RCNN for the point+voxel-based approach. The performance of each model was compared using the mean average precision (mAP) metric. RESULTS : While all models exhibited a strong performance, PV-RCNN achieved the highest performance across easy, moderate, and hard difficulty levels. PV-RCNN outperformed the other models in bounding box (Bbox), bird’s eye view (BEV), and 3D object detection tasks. These results highlight PV-RCNN's ability to maintain a high performance across diverse driving environments by combining the efficiency of the voxel-based method with the precision of the point-based method. These findings provide foundational insights not only for automated vehicles but also for traffic detection, enabling the accurate detection of various objects in complex road environments. In urban settings, models such as PV-RCNN may be more suitable, whereas in situations requiring real-time processing efficiency, the voxelbased PointPillars model could be advantageous. These findings offer important insights into the model that is best suited for specific scenarios. CONCLUSIONS : The findings of this study aid enhance the safety and reliability of automated driving systems by enabling vehicles to perceive their surroundings accurately and avoid potential hazards at an early stage. Furthermore, the use of LiDAR sensors for traffic monitoring is expected to optimize traffic flow by collecting and analyzing real-time traffic data from road environments.

PURPOSES : The evaluation of the low-temperature performance of an asphalt mixture is crucial for mitigating transverse thermal cracking and preventing traffic accidents on expressways. Engineers in pavement agencies must identify and verify the pavement sections that require urgent management. In early 2000, the research division of the Korea Expressway Corporation developed a three-dimensional (3D) pavement condition monitoring profiler vehicle (3DPM) and an advanced infographic (AIG) highway pavement management system computer program. Owing to these efforts, the management of the entire expressway network has become more precise, effective, and efficient. However, current 3DPM and AIG technologies focus only on the pavement surface and not on the entire pavement layer. Over the years, along with monitoring, further strengthening and verification of the feasibility of current 3DPM and AIG technologies by performing extensive mechanical tests and data analyses have been recommended. METHODS : First, the pavement section that required urgent care was selected using the 3DPM and AIG approaches. Second, asphalt mixture cores were acquired from the specified section, and a low-temperature fracture test, semi- circular bending (SCB) test, was performed. The mechanical parameters, energy-release rate, and fracture toughness were computed and compared. RESULTS : As expected, the asphalt mixture cores acquired from the specified pavement section ( poor condition – bad section) exhibited negative fracture performances compared to the control section (good section). CONCLUSIONS : The current 3DPM and AIG approaches in KEC can successfully evaluate and analyze selected pavement conditions. However, more extensive experimental studies and mathematical analyses are required to further strengthen and upgrade current pavement analysis approaches.

Insects, including aphids, caterpillars, and beetles, have a significant impact on biodiversity, ecology, and the economy by consuming various plant tissues like leaves, stems, and fruits, leading to issues such as holes, defoliation, and impaired growth. Consequently, our study's primary goal was to establish a model system capable of identifying and tracking insects, covering aspects like their behaviors, movements, sizes, and patterns. Our research has successfully produced a 3D monitoring system specifically designed for continuous insect tracking by applying it to brown planthopper. This technology allows for in-depth exploration of insect behaviors and their interactions with plants and crops. The potential applications of this technique are highly promising, offering valuable assistance to researchers in unraveling insect behavior and ecological dynamics and driving further advancements in these crucial research areas.

주기적이고 지속적으로 자료를 얻을 수 있는 위성영상은 지표면의 변화를 모니터링 하기 위한 매우 효과적인 자료이다. 위성영상을 이용한 기존의 변화탐지 연구는 두 시점의 지표 특성을 각각 분석해 서로 비교하여 변화를 밝혀내는 연구를 주로 해왔다. 그러나 최근에는 연속성을 갖는 다중 시기 위성영상으로부터 전체적인 경향이나 단기적인 변화를 찾아내는 연구에 관심이 높아지고 있다. 이 연구에서는 다중 시기 위성영상을 분석하기 위해 3차원 웨이블릿 변환 기반의 기법을 제안하고 테스트해보았다. 3차원 웨이블릿 변환을 이용하면 자료의 중요한 특성은 보존하면서 차원을 줄이는 것이 가능하다. 또한 다중 시기의 자료로부터 주요 패턴을 간추려 내고 공간, 시간적으로 인접한 주변 화소와의 관계를 파악할 수 있다. 연구 결과, 3차원 웨이블릿 변환 기법은 전체적인 경향성이나 특별한 변화 특성을 빠른 시간내에 밝혀내는 데 유용할 뿐만 아니라 분해 방향에 따라 각기 다른 정보를 제공해 주는 하위 밴드를 통해 새로운 정보를 얻을 수 있을 것으로 기대된다.

본 연구는 무인항공기를 활용한 원격탐사적 기법을 통해 고해상도 정사영상과 수치표고모델기 반 3차원 지표모델을 구축하여, 광해복구사업의 중간단계 모니터링에 활용하고 그 효율성을 고찰하였 다. 무인항공기를 통한 원격탐사로 3.8 cm의 공간해상도를 갖는 수치표고모델 및 정사영상을 구축하 였으며, 광해복구사업의 중간과정을 모니터링하였다. 또한 고해상도 영상을 통해 사물 및 지형적 구분 이 용이함을 확인하였다. 구축된 수치표고모델을 기반으로 3차원 모델을 구축하였고 토양복구사업의 면적 및 체적 등의 공간정보를 추출하였다. 그 결과 사업 결과모델 형성을 위한 추가적인 토양 적치 총량은 268,672 m3이며 약 71만 톤의 양에 해당하는 것을 확인하였다. 이는 무인항공기의 광해복구사 업 모니터링의 효율성을 증명하는 것으로 추후 보다 많은 활용도를 보일 것으로 사료된다.