• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.988-999
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• 2021

AOI (Automatic Optical Inspection) of PCB (Printed Circuit Board) is a very important step to guarantee the product performance. The process of registering components called teaching mode is first perform, and AOI is then carried out in a testing mode that checks defects, such as recognizing and comparing the component mounted on the PCB to the stored components. Since most of registration of the components on the PCB is done manually, it takes a lot of time and there are many problems caused by mistakes or misjudgement. In this paper, A components classifier is proposed using YOLO (You Only Look Once) v2's object detection model that can automatically register components in teaching modes to reduce dramatically time and mistakes. The network of YOLO is modified to classify small objects, and the number of anchor boxes was increased from 9 to 15 to classify various types and sizes. Experimental results show that the proposed method has a good performance with 99.86% accuracy.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.677-678
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• 2020

본 연구는 자율주행 시대에 자동차의 외부통제를 가능하게 하는데 목적이 있다. 자율주행 자동차의 외부통제를 하기 위해 교통경찰 수신호를 사용한다. 교통이라는 특별한 상황을 고려하여 실시간 객체 검출이 가능한 YOLO모델을 사용하였고, 수신호 데이터 학습을 위해 Data Argumentation 기법을 사용하여 데이터를 확보한 후 이를 바탕으로 YOLO모델을 학습하였다. 학습된 YOLO모델을 이용하여 교통의 흐름에서 교통 통제자를 실시간으로 검출하였다. 이후 검출된 객체를 이용하여 객체 확인 알고리즘과 수신호 의미파악 알고리즘을 사용하여 수신호의 의미를 파악하고 이를 사용자에게 전달한다. 이와 같은 시스템을 통해 자율주행 자동차에 돌발 상황 발생 시 보다 정확하고 빠르게 교통의 흐름을 정상화 할 수 있는 장점이 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.550-552
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• 2020

본 논문에서는 YOLO(You Only Look Once) 사물 인식 알고리즘을 활용하여 영상 압축에 적용한다. YOLO 는 물체의 일반화된 특징을 학습한 뉴럴 네트워크이다. 영상을 압축하는 동시에 YOLO 를 활용하여, 영상 내의 사물을 인식한다. 사물이 인식된 영역을 영상 압축을 할 때, 더 구체적으로 예측을 하는 방법을 제안한다. 본 논문에서 제안하는 방법은 QP(Quantization Parameter)를 조절하여, YOLO 로부터 인식된 사물을 더 정교하게 사물을 부호화/복호화한다. VVC(Versatile Video Coding) 기반에서 Rate-Control 를 사용하며, QP 를 조절한다. QP 는 CTU-Level 단위로 조절하며, 사물이 포함된 CTU 는 더 낮은 QP 를 바탕으로 효율적인 화질을 가져온다. 본 논문에서 제안하는 방법은 VVC 기반으로 한 Rate-Control 보다 주관적 화질이 선명한 것으로 보인다.

• 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 the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.813-826
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• 2022

AI-based image processing technologies in various fields have been widely studied. However, the lighter the board, the more difficult it is to reduce the weight of image processing algorithm due to a lot of computation. In this paper, we propose a method using deep learning for object recognition algorithm in lightweight embedded boards. We can determine the area using a deep neural network architecture algorithm that processes semantic segmentation with a relatively small amount of computation. After masking the area, by using more accurate deep learning algorithm we could operate object detection with improved accuracy for efficient neural network (ENet) and You Only Look Once (YOLO) toward executing object recognition in real time for lightweighted embedded boards. This research is expected to be used for autonomous driving applications, which have to be much lighter and cheaper than the existing approaches used for object recognition.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.103-108
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• 2024

In this paper, a smart elevator system was studied using real-time object detection technology based on YOLO(You only look once)v5. When an external elevator button is pressed, the YOLOv5 model analyzes the camera video to determine whether there are people waiting, and if it determines that there are no people waiting, the button is automatically canceled. The study introduces an effective method of implementing object detection and communication technology through YOLOv5 and MQTT (Message Queuing Telemetry Transport) used in the Internet of Things. And using this, we implemented a smart elevator system that determines in real time whether there are people waiting. The proposed system can play the role of CCTV (closed-circuit television) while reducing unnecessary power consumption. Therefore, the proposed smart elevator system is expected to contribute to safety and security issues.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.235-238
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• 2019

In real world object detection is an active research topic for understanding different objects from images. There are different models presented in past and had significant results. In this paper we are presenting vehicle logo detection using previous object detection models such as You only look once (YOLO) and Faster Region-based CNN (F-RCNN). Both the front and rear view of the vehicles were used for training and testing the proposed method. Along with deep learning an image pre-processing algorithm called perspective transformation is proposed for all the test images. Using perspective transformation, the top view images were transformed into front view images. This algorithm has higher detection rate as compared to raw images. Furthermore, YOLO model has better result as compare to F-RCNN model.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.183-186
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• 2022

최근 인공지능 기반 객체 탐지 기술이 발전함에 따라 영상 감시, 얼굴 인식, 로봇 제어, IoT, 자율주행, 제조업, 보안 등 다양한 분야에 활용되고 있다. 이에 본 논문은 발전된 객체 탐지 알고리즘을 이용하여 비전문가에겐 생소한 컴퓨터나 전기 장치 등의 '단자(terminal)' 모양을 구별하는 방법을 제안한다. 이를 위해 객체 탐지 프로그램인 You Only Look Once (YOLO) 알고리즘을 이용하여 입력한 단자들의 모양을 검출하는 알고리즘을 구성하였다. 일상에서 쉽게 볼 수 있는 단자들의 이미지(VGA, DVI, HDMI, DP, USB-A, USB-C)를 라벨링하여 데이터셋을 구축하였고, YOLOv4와 YOLOv5 두 버전의 알고리즘을 사용하여 성능을 검증하였다. 실험 결과 mean Average Precision(mAP) 기준 최대 92.9%의 정확도를 얻을 수 있었다. 전기 장치에 따라 단자의 모양이 다양하고, 그 종류 또한 많기 때문에 본 연구가 방송 기술 등의 여러 분야에 응용될 것으로 기대된다.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.495-505
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• 2019

Typical algorithms of deep learning include CNN(Convolutional Neural Networks), which are mainly used for image recognition, and RNN(Recurrent Neural Networks), which are used mainly for speech recognition and natural language processing. Among them, CNN is able to learn from filters that generate feature maps with algorithms that automatically learn features from data, making it mainstream with excellent performance in image recognition. Since then, various algorithms such as R-CNN and others have appeared in object detection to improve performance of CNN, and algorithms such as YOLO(You Only Look Once) and SSD(Single Shot Multi-box Detector) have been proposed recently. However, since these deep learning-based detection algorithms determine the success of the detection in the still images, stable object tracking and detection in the video requires separate tracking capabilities. Therefore, this paper proposes a method of combining Kalman filters into deep learning-based detection networks for improved object tracking and detection performance in the video. The detection network used YOLO v2, which is capable of real-time processing, and the proposed method resulted in 7.7% IoU performance improvement over the existing YOLO v2 network and 20 fps processing speed in FHD images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.141-143
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• 2022

This paper presented a new technology to identify traffic violations of motorbike riders by detecting the traffic signal using You Only Look Once (YOLO) object detection. The hardware module that is mounted on the front of the motorbike consists of Raspberry Pi with a camera to run the YOLO object detection, a GPS module to acquire the motorcycle's coordinate, and a LoRa communication module to send the data to a cloud DB. The main goal of the software is to determine whether a motorbike has violated a traffic signal. This paper proposes a function to recognize the red traffic signal colour with its movement inside the camera angle and determine that the traffic signal violation happens if the traffic signal is moving to the right direction (the rider turns left) or moving to the top direction (the riders goes straight). Furthermore, if a motorbike rider is violated the signal, the rider's personal information (name, mobile phone number, etc), the snapshot of the violation situation, rider's location, and date/time will be sent to a cloud DB. The violation information will be delivered to the driver's smartphone as a push notification and the local police station to be used for issuing violation tickets, which is expected to prevent motorbike riders from violating traffic signals.