• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.443-444
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• 2019

본 논문에서는 라즈베리파이(Raspberry Pi)와 실시간 객체 감지 기술인 YOLO를 이용한 전기차충전기앞불법주차 경고 영상인식 시스템을 제안한다. 최근 전기 자동차의 사용과 더불어 충전 인프라는 점점 늘어나는 중이지만, 여전히 전기차 충전기는 많지 않은 것이 현실이다. 전국 1,000여 곳이 넘는 전기차 충전소에 대해 법령으로 인한 규제를 시행 중임에도 불구하고 불법주차를 하는 일반차 오너들은 여전히 많다. 이로 인해 전기차 오너들은 충전에 많은 불편함이 있다. 이 시스템은 전기 자동차의 번호판을 인식하여 실시간 객체 감지 딥러닝 기법인 YOLO를 이용해 전기 자동차의 번호판에 특정 부분을 인식하고 특정 부분이 없는 일반 자동차가 전기차 충전기 앞 불법 주차를 하게 되면 부저와 LED경고를 통해 주차된 일반 차량에게 경고를 하여, 불법 주차자와 더불어 주변을 지나가는 행인들에게도 전기차 앞 불법 주차에 대해 각인을 시켜줄 수 있는 시스템이다.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.77-83
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• 2022

In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.

• Journal of Practical Engineering Education
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• v.13 no.3
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• pp.491-496
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• 2021

In this thesis, we intend to use CCTVs installed in existing parking lots to understand the current status of parking lots and provide real-time information to users through Android applications. It describes how to set the ROI in the parking area using YOLO V3 and how to provide the number of vacancies that change in real time through the set ROI, and describes how to link CCTV-server-user using IMAGE ZMQ and FIREBASE. The user can know the real-time situation of the parking lot near the destination before arriving through the application and can come up with various measures accordingly.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.83-89
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• 2021

In this paper, we propose an algorithm that provide services to consumers who want to conduct business by scientifically and systematically analyzing the number of pedestrians in a specific area over a specific period of time. In this paper, we proposed the algorithm to analyze the commercial area using the pedestrian-detect algorithm in the particular region using YOLO, one of the deep learning techniques. And with one image per minute in the images, the number of pedestrians is identified and this information is used for the analysis of business district on interesting area and time, systematically and objectively.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.222-224
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• 2022

Korea has more than 2,000 fires and more than 2,000 casualties every year. This study takes measures to facilitate the incorporation of 119 fire trucks by judging vehicles or standing signs using real-time image reading YOLO5 before the fire trucks arrive at the fire site. It is possible to shorten the time to extinguish a fire by photographing a fire site, transmitting the situation of the site, and analyzing the components of smoke to determine the type of fire. As a result, it is expected that it will be able to minimize casualties by keeping the golden time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.840-860
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• 2023

Aiming at the serious occlusion and slow tracking speed in pedestrian target tracking and recognition in complex scenes, a target tracking method based on improved YOLO v5 combined with Deep SORT is proposed. By merging the attention mechanism ECA-Net with the Neck part of the YOLO v5 network, using the CIoU loss function and the method of CIoU non-maximum value suppression, connecting the Deep SORT model using Shuffle Net V2 as the appearance feature extraction network to achieve lightweight and fast speed tracking and the purpose of improving tracking under occlusion. A large number of experiments show that the improved YOLO v5 increases the average precision by 1.3% compared with other algorithms. The improved tracking model, MOTA reaches 54.3% on the MOT17 pedestrian tracking data, and the tracking accuracy is 3.7% higher than the related algorithms and The model presented in this paper improves the FPS by nearly 5 on the fps indicator.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.31-33
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• 2021

In this paper, a dataset was designed and built to improve the accuracy of finger direction detection using an object detection algorithm based on You Only Look Once (YOLO). In order to improve the object detection performance, about 200 finger image data sets were trained, and to confirm that the detection accuracy differs from each other according to the angle of the palm, 50 comparison groups of different angles were configured and tested. As a result of the experiment, it was confirmed that the detection accuracy of palm located in a direction close to 90° is higher than that of other angles.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.723-726
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• 2021

현재 단일 이미지에서 Object Detection 성능은 매우 좋은 편이다. 하지만 동영상에서는 처리 속도가 너무 느리고 임베디드 시스템에서는 real-time이 힘든 상황이다. 연구 논문에서는 하이엔드 GPU에서 다른 기능 없이 YOLO만 구동했을 때 real-time이 가능하다고 하지만 실제 사용자들은 상대적으로 낮은 사양의 GPU를 사용하거나 CPU를 사용하기 때문에 일반적으로는 자연스러운 real-time을 하기가 힘들다. 본 논문에서는 이러한 제한점을 해결하고자 계산량이 많은 Object Detection model 사용을 줄이는 방안은 제시하였다. 현재 Video영상에서 Object Detection을 수행할 때 매 frame마다 YOLO모델을 구동하는 것에서 YOLO 사용을 줄임으로써 계산 효율을 높였다. 본 논문의 알고리즘은 카메라가 움직이거나 배경이 바뀌는 상황에서도 사용이 가능하다. 속도는 최소2배에서 ~10배이상까지 개선되었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.51-53
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• 2021

유해조수에 의한 농작물 피해규모는 2015 년 106 억원, 2017 년 126 억원에 이어 2019 년 137 억원으로 해마다 늘어나고 있다. 유해조수 중 조류에 의한 피해는 농작물 외에도 항공기, 전기/통신망, 양식장에 이르기 까지 다양한 산업분야에서 발생한다. ICT 기술은 유해조수에 의한 농작물 및 시설물의 피해를 줄이기 위한 효과적인 방안을 제시할 수 있다. 본 연구에서는 이미지 인식 및 분석 기술을 이용하여 유해조수 감지 및 피해방지를 위한 YOLO 기반의 감지 모델을 설계 후 유해조수 중 조류에 적용하여 테스트했다. 제안하는 모델은 여러 산업분야에서 유해조수 피해 방지를 위한 다양한 응용개발에 활용될 수 있다.