• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1218-1230
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• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.2
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• pp.359-363
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• 2019

As commercial drones have been widely used, concerns for collision accidents with people and invading secured properties are emerging. The detection of drone is a challenging problem. The deep learning based object detection techniques for detecting drones have been applied, but limited to the specific cases such as detection of drones from bird and/or background. We have tried not only detection of drones, but classification of different drones with an end-to-end model. YOLOv2 is used as an object detection model. In order to supplement insufficient data by shooting drones, data augmentation from collected images is executed. Also transfer learning from ImageNet for YOLOv2 darknet framework is performed. The experimental results for drone detection with average IoU and recall are compared and analysed.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.302-309
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• 2022

Occupational safety accidents are caused by various factors, and it is difficult to predict when and why they occur, and it is directly related to the lives of workers, so the interest in safety accidents is increasing every year. Therefore, in order to reduce safety accidents at industrial fields, workers are required to wear personal protective equipment. In this paper, we proposes a method to automatically check whether workers are wearing safety helmets among the protective equipment in the industrial field. It detects whether or not the helmet is worn using YOLOv5, a computer vision-based deep learning object detection algorithm. We transfer learning the s model among Yolov5 models with different learning rates and epochs, evaluate the performance, and select the optimal model. The selected model showed a performance of 0.959 mAP.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.173-174
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• 2023

In this paper, we introduce a model for detecting Personal Protective Equipment (PPE) using YOLO (You Only Look Once), an object detection neural network. PPE is used to maintain a safe working environment, and proper use of PPE protects workers' safety and health. However, failure to wear PPE or wearing it improperly can cause serious safety issues. Therefore, a PPE detection system is crucial in industrial settings.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.233-247
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• 2023

Recently, research on detecting objects in hidden spaces beyond the direct line-of-sight of observers has received attention. Most studies use optical equipment that utilizes the directional of light, but sound that has both diffraction and directional is also suitable for non-line-of-sight(NLOS) research. In this paper, we propose a novel method of detecting objects in non-line-of-sight (NLOS) areas using acoustic signals in the audible frequency range. We developed a deep learning model that extracts information from the NLOS area by inputting only acoustic signals and predicts the properties and location of hidden objects. Additionally, for the training and evaluation of the deep learning model, we collected data by varying the signal transmission and reception location for a total of 11 objects. We show that the deep learning model demonstrates outstanding performance in detecting objects in the NLOS area using acoustic signals. We observed that the performance decreases as the distance between the signal collection location and the reflecting wall, and the performance improves through the combination of signals collected from multiple locations. Finally, we propose the optimal conditions for detecting objects in the NLOS area using acoustic signals.

• Asia pacific journal of information systems
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• v.7 no.2
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• pp.163-183
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• 1997

As object models and hybrid knowledge are increasingly used in current information systems development, Is-a structures need to be more formally defined, and subsequently subsumption related anomalies need to be detected with minimal declaration of meta knowledge. This paper extends a metagraph in the hybrid environments and demonstrates its utilities for detecting such anomalies that can occur from semantics and dynamics unique to the hybrid knowledge and data structure.

• Proceedings of the Korea Concrete Institute Conference
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• 1997.10a
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• pp.791-796
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• 1997

Deterioration of pavements or tunnels primarily from the existence of voids under the pavements or tunnel linings. To detect these voids effectively by non-destructive testes, a method using radar was proposed. In this research, the detection of shape of voids by radar image processing is investigate. The experiments and simulation were conducted to detect voids in or under concrete pavements for tunnel linings) with reinforcing bars. From the results, the fundamental algorithm for tracing the voids, improving the horizontal resolution of the object image and detecting shape of objects, was verified.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.96-104
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• 1998

A new gray-scale thinning algorithm using local min/max operations is proposed. In this method, erosion and dilation properties of local min/max operations are using for generating new rides and detecting ridges in gray scale image, and gray-scale skeletons are gradually obtained by accumulating the detected ridges. This method can be applicable to the unsegmented image in which object are not specified, and the obtained skeletons correspond to the ridges (high gray values) of an input image.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.137-140
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• 2002

This paper presents a new variational framework for detecting and tracking moving objects in image sequence. Motion detection is performed using Level Set Model. The original frame is used to provide th moving object boundaries Then, the detection and the tracking problem are addressed in a common framework that employs a inward-outward curve evolution function. This function is minimized using a gradient decent method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2676-2683
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• 2009

The objective of this thesis is to improve the quality of a software product based on the enhancement of a software design quality using a better error detecting method. Also, this thesis is based on a software design method called as MOA(Methodology for Object to Agents) which uses an ontology based ODES(A Method based on Ontology for Detecting Errors in the Software Design) model as a common information model. At this thesis, a new format of error detecting method was defined. The method is implemented during a transformation process from UML model to ODES model using a ODES model, a Inter-View Inconsistency Detection technique and a combination of ontologic property of consistency framework and related rules. Transformation process to ODES model includes lexicon analysis and meaning analysis of a software design using of multiple mapping table at algorithm for the generation of ODES model instance.