• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 한국정보통신학회 2018년도 춘계학술대회
• /
• pp.671-674
• /
• 2018

In this study, we implement a YOLO driving assistance system using a model car. The YOLO is an object detection and recognition algorithm using deep running which is becoming an issue recently. The system alerts the lane departure by applying the image processing technology to the image acquired through the camera, recognizes the objects using the YOLO, and performs various functions according to the type of the object and the distance between the vehicle. the YOLO, which is superior to the existing object detection and recognition algorithm, improves the performance of the driving assist system without additional equipment. The driving assist system using the YOLO will ensure the safety of the driver with low cost.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 한국컴퓨터정보학회 2020년도 제62차 하계학술대회논문집 28권2호
• /
• pp.285-286
• /
• 2020

전 세계적으로 유행하는 COVID-19(코로나19)로 인해 사람들은 대면 접촉을 피하게 되었고, 전염성이 높은 이유로 마스크의 착용이 의무화되고 있고, 이를 검사하는 업무가 증가하고 있다. 그래서, 인공지능 기술을 통해 업무를 도와줄 수 있는 출입자 통계와 출입자 마스크 착용 검사를 할 수 있는 시스템이 필요하다. 이를 위해 본 논문에서는 딥러닝 알고리즘을 활용한 출입자 통계와 마스크 착용 판별 시스템을 제시한다. 또한, 실시간 영상인식에 많이 활용되고 있는 YOLO-v3와 YOLO-v4, YOLO-Tiny 알고리즘을 데스크탑 PC와 Nvidia사의 Jetson Nano에 적용하여 알고리즘별 성능 비교를 통해 적합한 방법을 찾고 적용하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• 제18권6호
• /
• pp.155-163
• /
• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• Journal of Advanced Navigation Technology
• /
• 제27권6호
• /
• pp.776-781
• /
• 2023

K-UAM will be commercialized through maturity after 2035. Since the Urban Air Mobility (UAM) corridor will be used vertically separating the existing helicopter corridor, the corridor usage is expected to increase. Therefore, a system for monitoring corridors is also needed. In recent years, object detection algorithms have developed significantly. Object detection algorithms are largely divided into one-stage model and two-stage model. In real-time detection, the two-stage model is not suitable for being too slow. One-stage models also had problems with accuracy, but they have improved performance through version upgrades. Among them, YOLO-V5 improved small image object detection performance through Mosaic. Therefore, YOLO-V5 is the most suitable algorithm for systems that require real-time monitoring of wide corridors. Therefore, this paper trains YOLO-V5 and analyzes whether it is ultimately suitable for corridor monitoring.K-uam will be commercialized through maturity after 2035.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
• /
• pp.187-188
• /
• 2021

본 논문에서는 실시간으로 web-cam을 이용해, 후숙과일의 불량 여부를 판단, 분류하고 불량이 없는 후숙과일의 이미지 분석을 통하여 숙성도 예측하는 시스템을 소개한다. 실시간 다중 객체인식에 탁월한 yolo모델을 활용해, 과일의 불량여부 판단 후 분류하고, 이미지를 획득한 뒤, k-mean clustering 알고리즘을 이용해, 이미지를 segmentation 한다. segmentation된 이미지에 grabcut 알고리즘의 foreground-extraction을 사용해 배경 제거를 한 뒤, cluster의 중심색상값 색상값의 면적%, 전체 면적을 이용해 현재 숙성도를 계산하고 이를 이용해 과일의 후숙 시간 데이터와 비교, 숙성이 완료될 시간을 예측한다. 기존 수작업으로 이루어지고 있는 과일의 분류작업의 인력 감소 및 정확성을 높일 수 있는 알고리즘을 제안한다.

• Journal of the Korean Association of Geographic Information Studies
• /
• 제26권4호
• /
• pp.27-39
• /
• 2023

Detecting greenhouses from the remote sensing datasets is useful in identifying the illegal agricultural facilities and predicting the agricultural output of the greenhouses. This research proposed a methodology for automatically detecting greenhouses from a given Planetscope satellite imagery acquired in the areas of Gimje City using the deep learning technique through a series of steps. First, multiple training images with a fixed size that contain the greenhouse features were generated from the five training Planetscope satellite imagery. Next, the YOLO(You Only Look Once) model was trained using the generated training images. Finally, the greenhouse features were detected from the input Planetscope satellite image. Statistical results showed that the 76.4% of the greenhouse features were detected from the input Planetscope satellite imagery by using the trained YOLO model. In future research, the high-resolution satellite imagery with a spatial resolution less than 1m should be used to detect more greenhouse features.

• The Journal of the Korea institute of electronic communication sciences
• /
• 제16권6호
• /
• pp.1329-1338
• /
• 2021

There are rarely effective methods to help visually impaired people when they want to know the expiration date of products excepted to only Braille. In this study, we developed an expiration date notification system based on YOLO and OCR for visually impaired people. The handicapped people can automatically know the expiration date of a specific product by using our system without the help of a caregiver, fast and accurately. The proposed system is worked by four different steps: (1) identification of a target product by scanning its barcode; (2) segmentation of an image area with the expiration date using YOLO; (3) classification of the expiration date by OCR: (4) notification of the expiration date by TTS. Our system showed an average classification accuracy of about 86.00% when blindfolded subjects used the proposed system in real-time. This result validates that the proposed system can be potentially used for visually impaired people.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• 제25권1호
• /
• pp.36-43
• /
• 2021

In this research, the accuracy of YOLO v3 algorithm in object detection during AGV (Automated Guided Vehicle) operation was investigated. First of all, AGV with 2D LiDAR and stereo camera was prepared. AGV was driven along the route scanned with SLAM (Simultaneous Localization and Mapping) using 2D LiDAR while front objects were detected through stereo camera. In order to evaluate the accuracy of YOLO v3 algorithm, recall, AP (Average Precision), and mAP (mean Average Precision) of the algorithm were measured with a degree of machine learning. Experimental results show that mAP, precision, and recall are improved by 10%, 6.8%, and 16.4%, respectively, when YOLO v3 is fitted with 4000 training dataset and 500 testing dataset which were collected through online search and is trained additionally with 1200 dataset collected from the stereo camera on AGV.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
• /
• pp.697-698
• /
• 2021

본 논문에서는 시각 장애우의 식품 안전성 증진을 위해 광학 문자 인식 (optical character recognition, OCR) 및 실시간 객체 인식 (you only look once, YOLO) 알고리즘에 기반한 식품의 유통기한 자동 알림 시스템을 제안한다. 제안하는 시스템은 1) 스마트폰 카메라를 통해 실시간으로 입력되는 영상에서 YOLO 알고리즘을 활용하여 유통기한으로 예측되는 이미지 영역을 검출하고, 2) 검출된 영역에서 OCR 알고리즘을 활용하여 유통기한 데이터를 추출하며, 3) 최종 추출된 유통기한 데이터를 음성으로 변환하여 시각 장애우에게 전달한다. 개발된 시스템은 유통기한 정보를 추출해서 사용자에게 전달하기까지 평균 약 7초 이내의 빠른 응답 속도를 보였으며, 62.8%의 객체 인식 정확도와 93.6%의 문자 인식 정확도를 보였다. 이러한 결과들은 제안하는 시스템을 시각 장애우들이 실용적으로 활용할 수 있다는 가능성을 보여준다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 한국정보통신학회 2021년도 춘계학술대회
• /
• pp.28-30
• /
• 2021

In this paper, an algorithm that detects the user's finger direction using the YOLO (You Only Look Once) library was proposed. The processing stage of the proposed finger direction detection algorithm consists of a learning data management stage, a data learning stage, and a finger direction detection stage. As a result of the experiment, it was found that the distance between the camera and the finger had a very large influence on the accuracy of detecting the direction of the finger. We plan to apply this function to Turtlebot3 after improving the accuracy and reliability of the proposed algorithm in the future.