• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.1
• /
• pp.9-18
• /
• 2019

The intensity and the number of days of high temperature occurrence are also high and record heat occurred. In addition, the global warming phenomenon is intensifying globally, and especially in South Korea, the urban heat island phenomenon is also occurring due to rapid urbanization due to rapid industrial development. As the temperature of the city rises, it causes problems such as the comfort of the residential living and the cooling load. In this study, the cool roof performance is evaluated according to the roof color to reduce these problems. Unlike previous studies, UAV(Unmanned Aerial Vehicle) thermal infrared camera was used to obtain the surface temperature (white, grey, green, blue, brown, black) according to the rooftop color by remote sensing technique. As a result, the surface temperature of white color was $11{\sim}20^{\circ}C$ lower than other colors. Also air conditioning temperature of white color was $1.5{\sim}4.4^{\circ}C$ lower than other colors and the digital thermometer of white color was about $1.5{\sim}3.5^{\circ}C$ lower than other colors. It was confirmed that the white cool roof performance is the best, and the UAV and the thermal infrared camera can confirm the cool roof performa.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.1
• /
• pp.174-192
• /
• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• Spatial Information Research
• /
• v.15 no.2
• /
• pp.169-180
• /
• 2007

Applicability of Land-based MMS(Mobile Mapping System) having been increased gradually as digitalization of administrative operation and construction of integrated systems of the government and provincial government are growing up. As these requirements, the case can be occurred that the facilities should be surveyed rapidly in the specific area. At this case, the real time field processing method is more necessary than the post processing method and data processing speed should be an essential element as important as accuracy. In this study, the two space intersection methods used in photogrammetry were programmed and compared with each other to select more proper method for the three dimensional positioning in the field processing. Especially, at the analytic space intersection, the traditional close range terrestrial photogrammetry was modified and applied to that to adapt to MMS's characteristics that camera position and attitude are changed according to the vehicle movement. As a result, the difference of the accuracy between two methods is not significant but at the calculation time, the analytic space intersection is faster three times than the space intersection using collinearity condition.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.7
• /
• pp.1556-1567
• /
• 2009

The death rate of pedestrian in car accidents in Korea is 2.5 times higher than the average of OECD countries'. If a system that can detect pedestrians and send alarm to drivers is built and reduces the rate, it is worth developing such a pedestrian detection system (PDS). Since the accident rate in which pedestrians are involved is higher at nighttime than in daytime, the adoption of nighttime PDS is being standardized by big auto companies. However, they are usually using night visions or multiple sensors, which are usually expensive. In this paper we suggest a method for nighttime PDS using single wide dynamic range (WDR) monochrome camera in visible spectrum band. In our experiments, pedestrians were accurately detected if only most edges of pedestrians could be obtained.

• Journal of Korean Society of Transportation
• /
• v.29 no.4
• /
• pp.125-138
• /
• 2011

The highway visibility reduction caused by fog is one of the major elements of traffic accidents. Though the fog warning systems can lead drivers into safe driving by letting them aware dangerous situations in advance, the optical sensors, such as fog sensor, has been extremely costly. Through recent studies, it is delivered that visibility measurements have become obtainable with relatively cheap cameras and their functionality is as similar as a driver' visual sense. Those measurements however require additional signs or ROI, so it is still costly and unable to utilize the conventional images from the existing systems. This study proposes a new method to detect the visibility in real time based on the conventional images from the existing CCTV cameras. The proposed method builds a road model and extracts and applies vehicle movements and visible lines - those highlight easy and quick visibility measurements. The proposed method has advantages of both (1) having possible day and night visibility measurements similar to drivers' visual sense and (2) being easily applied to the existing CCTV system without additional devices. This paper presents field experiments using images acquired from the Central Inland Expressway and discusses future research directions.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.5
• /
• pp.923-930
• /
• 2016

Various pipes are used in the many industrial field such as water supply, drainage system and marine plants, so a maintenance of these pipes is essential. Especially, the maintenance of the piping in the industrial field, some professional staffs enter and clean the pipe. If the professional staffs can not enter and clean the pipe, the workers has to use the method of inserting a scraper connected to wire inside the pipe. However, this method demands huge budget and causes a number of problems such as traffic congestion. To solve these problems, pipe cleaning robot has been researching and developing. Many Pipe cleaning robots have a problem, that is impossible to confirm the operating condition of the robot in a real time. This paper suggest pipe cleaning robot with RS-485 which transmit operating and cleaning condition of robot and inner pipe filmed by camera, that user can check.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.10
• /
• pp.162-170
• /
• 2013

In recent years, the interests and needs of the Pedestrian Protection System (PPS), which is mounted on the vehicle for the purpose of traffic safety improvement is increasing. In this paper, we propose a pedestrian candidate window extraction and unit cell histogram based HOG descriptor calculation methods. At pedestrian detection candidate windows extraction stage, the bright ratio of pedestrian and its circumference region, vertical edge projection, edge factor, and PCA reconstruction image are used. Dalal's HOG requires pixel based histogram calculation by Gaussian weights and trilinear interpolation on overlapping blocks, But our method performs Gaussian down-weight and computes histogram on a per-cell basis, and then the histogram is combined with the adjacent cell, so our method can be calculated faster than Dalal's method. Our PCA reconstruction error based pedestrian detection candidate window extraction method efficiently classifies background based on the difference between pedestrian's head and shoulder area. The proposed method improves detection speed compared to the conventional HOG just using image without any prior information from camera calibration or depth map obtained from stereo cameras.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.6
• /
• pp.331-345
• /
• 2021

Virtual testing is considered a major requirement for the safety verification of autonomous driving functions. For virtual testing, both the autonomous vehicle and the driving environment should be modeled appropriately. In particular, a realistic modeling of the perception sensor system such as the one having a camera and radar is important. However, research on modeling to consistently generate realistic perception results is lacking. Therefore, this paper presents a sensor modeling method to provide realistic object detection results in a MILS (Model in the Loop Simulation) environment. First, the key parameters for modeling are defined, and the object detection characteristics of actual cameras and radar sensors are analyzed. Then, the detection characteristics of a sensor modeled in a simulation environment, based on the analysis results, are validated through a correlation coefficient analysis that considers an actual sensor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.6
• /
• pp.663-670
• /
• 2020

In the present study, we assessed the accuracy of aerial triangulation using a UAV (Unmanned Aerial Vehicle) capable of network RTK (Real-Time Kinematic) survey in a disaster situation that may occur in a semi-urban area mixed with buildings. For a reliable survey of check points, they were installed on the roofs of buildings, and static GNSS (Global Navigation Satellite System) survey was conducted for more than four hours. For objective accuracy assessment, coded aerial targets were installed on the check points to be automatically recognized by software. At the instance of image acquisition, the 3D coordinates of the UAV camera were measured using VRS (Virtual Reference Station) method, as a kind of network RTK survey, and the 3-axial angles were achieved using IMU (Inertial Measurement Unit) and gimbal rotation measurement. As a result of estimation and update of the interior and exterior orientation parameters using Agisoft Metashape, the 3D RMSE (Root Mean Square Error) of aerial triangulation ranged from 0.153 m to 0.102 m according to the combination of the image overlap and the angle of the image acquisition. To get higher aerial triangulation accuracy, it was proved to be effective to incorporate oblique images, though it is common to increase the overlap of vertical images. Therefore, to conduct a UAV mapping in an urgent disaster site, it is necessary to acquire oblique images together rather than improving image overlap.

• Journal of Broadcast Engineering
• /
• v.27 no.2
• /
• pp.228-239
• /
• 2022

In this paper, we propose an unmanned vehicle scratch detection deep learning model for car sharing services. Conventional scratch detection models consist of two steps: 1) a deep learning module for scratch detection of images before and after rental, 2) a manual matching process for finding newly generated scratches. In order to build a fully automatic scratch detection model, we propose a one-step unmanned scratch detection deep learning model. The proposed model is implemented by applying transfer learning and fine-tuning to the deep learning model that detects changes in satellite images. In the proposed car sharing service, specular reflection greatly affects the scratch detection performance since the brightness of the gloss-treated automobile surface is anisotropic and a non-expert user takes a picture with a general camera. In order to reduce detection errors caused by specular reflected light, we propose a preprocessing process for removing specular reflection components. For data taken by mobile phone cameras, the proposed system can provide high matching performance subjectively and objectively. The scores for change detection metrics such as precision, recall, F1, and kappa are 67.90%, 74.56%, 71.08%, and 70.18%, respectively.