• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.332-334
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• 2021

As fatal accidents due to industrial accidents and deaths due to civil accidents were pointed out as social problems, the Act on Punishment of Serious Accidents Occurred in the Workplace was enacted to ensure the safety of citizens and to prevent serious accidents in advance. Effort is required. In this paper, we propose a distance prediction model in relation to the case where an operator is hit by heavy equipment such as a forklift. For the data, actual forklift trucks and workers roaming environments were directly captured by CCTV, and it was conducted based on the Euclidean distance. It is thought that it will be possible to learn YOLO-v4 by directly building a data-set at the industrial site, and then implement a model that predicts the distance and determines whether it is a dangerous situation, which can be used as basic data for a comprehensive risk situation judgment model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.177-182
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• 2024

In this paper, the purpose of this paper is to predict and prevent the risk of crowd concentration in advance for possible future crowd accidents based on the Itaewon crush accident in Korea on October 29, 2022. In the case of a single CCTV, the administrator can determine the current situation in real time, but since the screen cannot be seen throughout the day, objects are detected using YOLOv4, which learns images taken with CCTV angle, and safety accidents due to crowd concentration are prevented by notification when the number of clusters exceeds. The reason for using the YOLO v4 model is that it improves with higher accuracy and faster speed than the previous YOLO model, making object detection techniques easier. This service will go through the process of testing with CCTV image data registered on the AI-Hub site. Currently, CCTVs have increased exponentially in Korea, and if they are applied to actual CCTVs, it is expected that various accidents, including accidents caused by crowd concentration in the future, can be prevented.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.4
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• pp.311-323
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• 2023

This paper introduces the industrial problem, the solution, and the results of the research conducted with Define Inc. The client company wanted to improve the performance of an object detection model on the floor plan dataset. To solve the problem, we analyzed the operational principles, advantages, and disadvantages of the existing object detection model, identified the characteristics of the floor plan dataset, and proposed to use of YOLO v5 as an appropriate object detection model for training the dataset. We compared the performance of the existing model and the proposed model using mAP@60, and verified the object detection results with real test data, and found that the performance increase of mAP@60 was 0.08 higher with a 25% shorter inference time. We also found that the training time of the proposed YOLO v5 was 71% shorter than the existing model because it has a simpler structure. In this paper, we have shown that the object detection model for the floor plan dataset can achieve better performance while reducing the training time. We expect that it will be useful for solving other industrial problems related to object detection in the future. We also believe that this result can be extended to study object recognition in 3D floor plan dataset.

• Smart Media Journal
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• v.10 no.4
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• pp.80-90
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• 2021

Using optical music recognition technology based on deep learning, we propose to apply the results derived to VR games. To detect the music objects in the music sheet, the deep learning model used YOLO v5, and Hough transform was employed to detect undetected objects, modifying the size of the staff. It analyzes and uses BPM, maximum number of combos, and musical notes in VR games using output result files, and prevents the backlog of notes through Object Pooling technology for resource management. In this paper, VR games can be produced with music elements derived from optical music recognition technology to expand the utilization of optical music recognition along with providing VR contents.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.6-15
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• 2022

In this study, a traffic light recognition algorithm was implemented and validated for low-speed special purpose vehicles in an urban environment. Real-time image data using a camera and YOLO algorithm were applied. Two methods were presented to increase the accuracy of the traffic light recognition algorithm, and it was confirmed that the second method had the higher accuracy according to the traffic light type. In addition, it was confirmed that the optimal YOLO algorithm was YOLO v5m, which has over 98% mAP values and higher efficiency. In the future, it is thought that the traffic light recognition algorithm can be used as a dual system to secure the platform safety in the traffic information error of C-ITS.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.422-425
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• 2021

본 논문에서는 최근 다양한 분야에서 많이 활용되고 있는 YOLO와 EAST 신경망을 이미지 속 문자열 탐지문제에 적용해보고 이들의 성능을 비교분석 해 보았다. YOLO 신경망은 v3 이전 모델까지는 이미지 속 문자영역 탐지에 낮은 성능을 보인다고 알려졌으나, 최근 출시된 YOLOv4와 YOLOv5의 경우 다양한 형태의 이미지 속에 있는 한글과 영문 문자열 탐지에 뛰어난 성능을 보여줌을 확인하고 향후 문자 인식 분야에서 많이 활용될 것으로 기대된다.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.305-314
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• 2021

Currently, due to COVID-19, household waste has a lot of impact on the environment due to packaging of food delivery. In this paper, we design and implement Faster-RCNN, SSD, and YOLOv4 models for municipal waste detection and classification. The data set explores two types of plastics, which account for a large proportion of household waste, and the types of aluminum cans. To classify the plastic type and the aluminum can type, 1,083 aluminum can types and 1,003 plastic types were studied. In addition, in order to increase the accuracy, we compare and evaluate the loss value and the accuracy value for the detection of municipal waste classification using Faster-RCNN, SDD, and YoloV4 three models. As a final result of this paper, the average precision value of the SSD model is 99.99%, the average precision value of plastics is 97.65%, and the mAP value is 99.78%, which is the best result.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.291-292
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• 2024

국내의 산재 사고 사망 비율 중 대부분은 건설업이 차지하고 있으며 사망 원인 중 42.9%는 추락사가 차지하고 있다. 따라서 국내 사고 사망을 예방하기 위해서는 노동자의 생명을 지켜주는 안전 장비의 착용 여부가 중요하다. 본 논문에서는 객체 탐지에 사용되는 YOLO v4와 YOLO v4-TINY 알고리즘과 영상 처리에 사용되는 OpenCV를 이용하여 실시간 영상에서 안전모 미착용 인원을 감지하고 관리자에게 알려주는 시스템을 개발하였다. 이 시스템을 활용하여 건설 현장에서 현장 카메라로 안전모 미착용 인원을 실시간으로 검출하여 경고하므로써 작업자의 안전에 기여할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1795-1811
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• 2019

In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method. Firstly, more contextual and small-object vehicle information can be obtained by our Joint Feature Network (JFN). Secondly, our Evolved Region Proposal Network (EPRN) generates initial anchor boxes by adding an improved version of the region proposal network in this network, and at the same time filters out a large number of false vehicle boxes by soft-Non Maximum Suppression (NMS). Then, our Mask Network (MaskN) generates an example that includes the vehicle occlusion, the generator and discriminator can learn from each other in order to further improve the vehicle object detection capability. Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN). Through the evaluation experiment on the DETRAC benchmark dataset, we find that in terms of mAP, our method exceeds Faster-RCNN by 11.15%, YOLO by 11.88%, and EB by 1.64%. Besides, our algorithm also has achieved top2 comaring with MS-CNN, YOLO-v3, RefineNet, RetinaNet, Faster-rcnn, DSSD and YOLO-v2 of vehicle category in KITTI dataset.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.205-212
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• 2024

This study developed a deep learning model that distinguishes the front (with garnish) and the back (without garnish) surface of the dried semi-finished product (dried bukak) for screening operation before transfter the dried bukak to oil heater using robot's vacuum gripper. For deep learning model training and verification, RGB images for the front and back surfaces of 400 dry bukak that treated by data preproccessing were obtained. YOLO-v5 was used as a base structure of deep learning model. The area, surface information labeling, and data augmentation techniques were applied from the acquired image. Parameters including mAP, mIoU, accumulation, recall, decision, and F1-score were selected to evaluate the performance of the developed YOLO-v5 deep learning model-based surface detection model. The mAP and mIoU on the front surface were 0.98 and 0.96, respectively, and on the back surface, they were 1.00 and 0.95, respectively. The results of binary classification for the two front and back classes were average 98.5%, recall 98.3%, decision 98.6%, and F1-score 98.4%. As a result, the developed model can classify the surface information of the dried bukak using RGB images, and it can be used to develop a robot-automated system for the surface detection process of the dried bukak before deep frying.