• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.687-690
• /
• 2015

Forward collision warning systems(FCWS) and lane change assist systems(LCAS) need regions of interest for detecting lanes and objects as road regions. Watershed segmentation is effective algorithm that classify the road. That algorithm is split results appear differently depending on Watershed line with local minimum in the early part of the seed. If not road regions or vehicles combined the road's seed, It segment road with the others. For compensate the that defect, It has to adaptive change by road environment. The method is that image segmentate the several of regions of interest. Then It is set in a straight line that is detected in regions of interest. If It was detected cars on seed, seed is adjusted the location. And If It wasn't include the line, seed is adjusted the length for final decision the seed. We can detect the road region using the final seed that selected according to the road environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.4
• /
• pp.95-105
• /
• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.4
• /
• pp.190-211
• /
• 2022

This study investigated the effect of the shape and height of road signs on detection performance when detecting road signs with LiDAR, which is recognized as an essential sensor for autonomous vehicles. For the study, four types of road signs with the same area and material and different shapes were produced, and a road driving test was performed by installing a 32Ch rotating LiDAR on the upper part of the vehicle. As a result of comparing the shape of the point cloud and the NPC according to the shape of the road sign, It is expected that a distance of less than 40m is required to recognize the overall shape of a road sign using 32Ch LiDAR, and shapes such as triangles and rectangles are more advantageous than squares in securing the maximum point cloud from a long distance. As a result of the study according to the height of the road sign, At short distances (within 20m), if the height of the sign is raised to more than 2m, it deviates from the vertical viewing angle of the LiDAR and cannot express the complete point cloud shape. However, it showed a negligible effect compared to the near-field height change. These research results are expected to be utilized in the development of road facilities dedicated to LiDAR for the commercialization of autonomous cooperative driving technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.11 no.5
• /
• pp.125-135
• /
• 2012

This paper proposes the new object detection system with two laser-scanners and a camera for classifying the objects and predicting the location of objects on road street. This detection system could be applied the new C-ITS service such as ADAS(Advanced Driver Assist System) or (semi-)automatic vehicle guidance services using object's types and precise position. This study describes the some examples in other countries and feasibility of object detection system based on a camera and two laser-scanners. This study has developed the heterogenous sensor's fusion method and shows the results of implementation at road environments. As a results, object detection system at roadside infrastructure is a useful method that aims at reliable classification and positioning of road objects, such as a vehicle, a pedestrian, and obstacles in a street. The algorithm of this paper is performed at ideal condition, so it need to implement at various condition such as light brightness and weather condition. This paper should help better object detection and development of new methods at improved C-ITS environment.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.12
• /
• pp.158-164
• /
• 2015

Lane detection is one of the key parts among autonomous vehicle technologies because lane keeping and path planning are based on lane detection. Camera is used for lane detection but there are severe limitations such as narrow field of view and effect of illumination. On the other hands, Lidar sensor has the merits of having large field of view and being little influenced by illumination because it uses intensity information. Existing researches that use methods such as Hough transform, histogram hardly handle multiple lanes in the co-occuring situation of lanes and road marking. In this paper, we propose a method based on RANSAC and regularization which provides a stable and precise detection result in the co-occuring situation of lanes and road marking in highway scenarios. This is performed by precise lane point extraction using circular model RANSAC and regularization aided least square fitting. Through quantitative evaluation, we verify that the proposed algorithm is capable of multi lane detection with high accuracy in real-time on our own acquired road data.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.25 no.1
• /
• pp.99-107
• /
• 2014

Abruptly occurred obstacles on highway threaten driving safety. Radar draws the attention to the collision avoidance system because it can be fully operational in all weather, and day and night condition. This paper presents the design, implementation and performance test results of pulsed Doppler radar system for detection and warning of road obstacles. The system is designed to consider highway environment and detection capability about various fixed and moving obstacles. The system consists of 4 subsystems, which include antenna unit, transmitter and receiver unit, radar signal & data processing unit, and controller & display unit. The core technologies include clutter map based change detection for fixed obstacles detection, Doppler estimation for velocity detection of moving targets, and azimuth angle estimation method using monopulse for lane estimation and tracking. The design performance of the developed radar system is verified through experiments using a fixed reference target and moving vehicles in test highway.

• Fire Science and Engineering
• /
• v.33 no.1
• /
• pp.85-91
• /
• 2019

This study evaluated the fire detection performance of an automatic fire extinguishing system for road tunnels, which combines flame wavelength detection technology with flame image detection technology. This fusion technique to improve the fire detection capability can reduce the damage caused by the fire suppression by locating the fire source in the fire and discharging the pressurized water only at the fire source. Experiments were conducted to determine the position of a fire source when a $70cm{\times}70cm$ target was placed at a distance of 15 m, 20 m, 25 m, 30 m, and 35 m, respectively, in a situation where there is a flame and smoke in a tunnel. The performance of the ultraviolet and triple wavelength infrared (IR3) sensors was attenuated due to the interference of thick smoke. In addition when the flame was blocked by thick smoke, the image sensor sensed the smoke and emitted a fire signal.

• Journal of the Institute of Convergence Signal Processing
• /
• v.10 no.3
• /
• pp.164-169
• /
• 2009

This paper proposes a road detection method using BP(Back-Propagation) neural network based on texture information of the each candidate road region segmented for satellite images. To segment the candidate road regions, the histogram-based binarization method proposed by N.Otsu is firstly performed and the neighboring regions surrounding road regions are then removed. And after extracting the principal color using the histogram of the segmented foreground, the candidate road regions are classified into the regions within ${\pm}25$ of the principal color. Finally, the road regions are segmented using BP neural network based on texture information of the candidate regions. The texture information in this paper is calculated using co-occurrence matrix and is used as an input data of the BP neural network. The proposed method is based on the fact that the road has the constant intensity and shape. The experiment demonstrated the validity of the proposed method and showed 90% detection accuracy for the various images.

• Journal of Korea Multimedia Society
• /
• v.20 no.2
• /
• pp.144-152
• /
• 2017

Among the technologies for implementing autonomous vehicles, advanced driver assistance system is a key technology to support driver's safe driving. In the technology using the vision sensor having a high utility, various preprocessing methods are used prior to feature extraction for lane detection. However, in the existing methods, the unnecessary lane candidates such as cars, lawns, and road separator in the road area are false positive. In addition, there are cases where the lane candidate itself can not be extracted in the area under the overpass, the lane within the dark shadow, the center lane of yellow, and weak lane. In this paper, we propose an efficient preprocessing method using k-means clustering for image division and the HSV color model. When the proposed preprocessing method is applied, the true positive region is maximally maintained during the lane detection and many false positive regions are removed.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.10
• /
• pp.983-988
• /
• 2011

This paper presents a robust method for ground plane detection in vision-based applications based on a monocular sequence of images with a non-stationary camera. The proposed method, which is based on the reliable estimation of the homography between two frames taken from the sequence, aims at designing a practical system to detect road surface from traffic scenes. The homography is computed using a feature matching approach, which often gives rise to inaccurate matches or undesirable matches from out of the ground plane. Hence, the proposed homography estimation minimizes the effects from erroneous feature matching by the evaluation of the difference between the predicted and the observed matrices. The method is successfully demonstrated for the detection of road surface performed on experiments to fill an information void area taken place from geometric transformation applied to captured images by an in-vehicle camera system.