• Korean Journal of Artificial Intelligence
• /
• v.11 no.3
• /
• pp.17-22
• /
• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.24 no.2
• /
• pp.127-136
• /
• 2016

The camera based object detection systems should satisfy the recognition performance as well as real-time constraints. Particularly, in safety-critical systems such as Autonomous Emergency Braking (AEB), the real-time constraints significantly affects the system performance. Recently, multi-core processors and system-on-chip technologies are widely used to accelerate the object detection algorithm by distributing computational loads. However, due to the advanced hardware, the complexity of system architecture is increased even though additional hardwares improve the real-time performance. The increased complexity also cause difficulty in migration of existing algorithms and development of new algorithms. In this paper, to improve real-time performance and design complexity, a task scheduling strategy is proposed for visual object tracking systems. The real-time performance of the vision algorithm is increased by applying pipelining to task scheduling in a multi-core processor. Finally, the proposed task scheduling algorithm is applied to crosswalk detection and tracking system to prove the effectiveness of the proposed strategy.

• Journal of Information Technology Services
• /
• v.6 no.3
• /
• pp.241-249
• /
• 2007

This paper describes a real-time face tracking system using effective detector and Kalman filter. In the proposed system, an image is separated into a background and an object using a real-time updated face color for effective face detection. The face features are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. The moving face is traced with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. The proposed system sets up an initial skin color and updates a region of a skin color through a moving skin color in a real time. It is possible to remove a background which has a similar color with a skin through updating a skin color in a real time. Also, as reducing a potential-face region using a skin color, the performance is increased up to 50% when comparing to the case of extracting features from a whole region.

• Journal of Gastric Cancer
• /
• v.21 no.1
• /
• pp.4-15
• /
• 2021

Recently, endoscopic screening systems have enabled the diagnosis of gastric cancer in the early stages. Early gastric cancer (EGC) is typically characterized by a shallow invasion depth and small size, which can hinder localization of EGC tumors during laparoscopic surgery. Here, we review nine recently reported tumor localization methods for the laparoscopic resection of EGCs. Preoperative dye or blood tattooing has the disadvantage of spreading. Preoperative 3-dimensional computed tomography reconstruction is not performed in real time during laparoscopic gastrectomy. Thus, they are considered to have a low accuracy. Intraoperative portable abdominal radiography and intraoperative laparoscopic ultrasonography methods can provide real-time feedback, but these methods require expertise, and it can be difficult to define the clips in some gastric regions. Despite a few limitations, intraoperative gastrofibroscopy provides real-time feedback with high accuracy. The detection system using an endoscopic magnetic marking clip, fluorescent clip, and radio-frequency identification detection system clip is considered highly accurate and provides real-time feedback; we expect a commercial version of this setup to be available in the near future. However, there is not yet an easy method for accurate real-time detection. We hope that improved devices will soon be developed and used in clinical settings.

• Journal of KIISE:Information Networking
• /
• v.31 no.2
• /
• pp.171-178
• /
• 2004

Port scanning detection systems should rather satisfy a certain level of the requirement for system performance like a low rate of “False Positive” and “False Negative”, and requirement for convenience for users to be easy to manage the system security with detection systems. However, public domain Real Time Scan Detection Systems have high rate of false detection and have difficulty in detecting various scanning techniques. In addition, as current real time scan detection systems are based on command interface, the systems are poor at user interface and thus it is difficult to apply them to the system security management. Hence, we propose TkRTSD(Tcl/Tk Real Time Scan Detection System) that is able to detect various scan attacks based on port scanning techniques by applying a set of new filter rules, and minimize the rate of False Positive by applying proposed ABP-Rules derived from attacker's behavioral patterns. Also a GUI environment for TkRTSD is implemented by using Tcl/Tk for user's convenience of managing network security.

• Korean Journal of Veterinary Research
• /
• v.47 no.4
• /
• pp.399-407
• /
• 2007

Cause of high lethality and dissemination to human being, new development of rapid method for the detection of highly pathogenic Avian Influenza Virus (AIV) is still necessary. For the detection of AIV subtype H5N1, typical pathogenic AIV, new method to confirm sub-typing of this virus is also needed. For the purpose of ultra-rapid detection and sub-typing of hemagglutinin and neuraminidase of AIV, this study was planned. As the results we could demonstrate an ultra-rapid multiplex real-time PCR (URMRT PCR) for the detection of AIV In this study, the URMRT PCR were optimized with synthesized AIV H5- and AIV Nl-specific DNA templates and GenSpector TMC, which is a semiconductor process technology based real-time PCR system with high frequencies of temperature monitoring. Under eight minutes, the amplifications of two AIV subtype-specific PCR products were successfully and independently detected by 30 cycled ultra-rapid PCR, including melting point analysis, from $1{\times}10^3$ copies of mixed template DNA. The URMRT PCR for the detection of AIV H5N 1 developed in this study could be expected to apply not only detections of different AIVs, but also various pathogens. It was also discussed that this kind of the fastest PCR based detection method could be improved by advance of related technology in near future.

• The Korean Journal of Mycology
• /
• v.46 no.4
• /
• pp.467-477
• /
• 2018

Colletotrichum circinans, an anthracnose pathogen, causes serious damage to onions worldwide. In this study, specific molecular markers were developed to detect C. circinans accurately and quickly with both conventional and real-time PCR methods. The cirTef-F/cirTef-R and cirTu-F/cirTu-R primer sets, which are specific for C. circinans, were constructed by analyzing $tef-1{\alpha}$ and ${\beta}-tubulin$ genes in the fungus. Using the conventional PCR method, 100 pg and 1 ng of fungal DNA could be detected using the cirTef-F/cirTef-R and cirTu-F/cirTu-R sets, respectively. Using the real-time PCR method, 10 pg and 100 pg of fungal DNA could be detected more sensitively with the cirTef-F/cirTef-R and cirTu-F/cirTu-R sets, respectively. Detection of C. circinans from the artificially infected onion seeds was possible by using both conventional and real-time PCR methods and the developed cirTef-F/cirTef-R primer set. The PCR markers specific for C. circinans developed in this study may enhance the efficiency of fungal pathogen detection in imported vegetables and seeds.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.2
• /
• pp.47-53
• /
• 2010

This paper proposed Residual Image detection and Color Info using the face detection technique. The proposed technique was fast processing speed and high rate of face detection on the video. In addition, this technique is to detection error rate reduced through the calibration tasks for tilted face image. The first process is to extract target image from the transmitted video images. Next, extracted image processed by window rotated algorithm for detection of tilted face image. Feature extraction for face detection was used for AdaBoost algorithm.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.1
• /
• pp.17-21
• /
• 2011

This paper presents a real- time detection of on-road succeeding vehicles based on low level edge features and a boosted cascade of Haar-like features. At first, the candidate vehicle location in an image is found by low level horizontal edge and symmetry characteristic of vehicle. Then a boosted cascade of the Haar-like features is applied to the initial hypothesized vehicle location to extract the refined vehicle location. The initial hypothesis generation using simple edge features speeds up the whole detection process and the application of a trained cascade on the hypothesized location increases the accuracy of the detection process. Experimental results on real world road scenario with processing speed of up to 27 frames per second for $720{\times}480$ pixel images are presented.

• Journal of information and communication convergence engineering
• /
• v.15 no.3
• /
• pp.187-192
• /
• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.