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

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.01a
• /
• pp.315-316
• /
• 2022

최근 전 세계적인 탄소 중립 정책으로 인해 전기차 보급 속도는 예상보다 훨씬 빠르게 증가하고 있다. 하지만 늘어나는 수요에 비해 전기차 충전기 수는 턱없이 부족하다. 그뿐만 아니라 일반 차들의 전기차 충전소 불법주차로 인해 전기차가 충전하지 못하는 불편함이 발생하고 있다. 본 논문에서는 에지 컴퓨터(edge computer)와 딥러닝 기반 객체 감지 시스템 YOLO(You only look once)를 이용한 전기차 충전소 불법주차 방지 시스템을 개발한다. 먼저, 이 시스템은 카메라를 통해 실시간으로 영상을 받아 YOLO를 이용하여 차량 번호판 인식이 되면 전기차 번호판의 특정 마크를 인식하여 전기차인지 일반 차인지를 판별하여 판별된 값에 따라 주차 차단기가 작동되는 시스템이다. 전기차이면 차단기가 내려가서 충전소를 이용할 수 있게 하고 일반차일 경우 주차 차단기가 내려가지 않고 막아 불법주차를 차단한다. 이와 같은 기술을 활용하여 전기차 충전소 불법주차 방지에 기여하고자 한다.

• Journal of the Society of Disaster Information
• /
• v.19 no.3
• /
• pp.665-672
• /
• 2023

Purpose: Underground utility tunnel is facility that is jointly house infrastructure such as electricity, water and gas in city, causing condensation problems due to lack of airflow. This paper aims to prevent electricity leakage fires caused by condensation by detecting whether the control panel door in the underground utility tunnel is open using a deep learning model. Method: YOLO, a deep learning object recognition model, is trained to recognize the opening and closing of the control panel door using video data taken by a robot patrolling the underground utility tunnel. To improve the recognition rate, image augmentation is used. Result: Among the image enhancement techniques, we compared the performance of the YOLO model trained using mosaic with that of the YOLO model without mosaic, and found that the mosaic technique performed better. The mAP for all classes were 0.994, which is high evaluation result. Conclusion: It was able to detect the control panel even when there were lights off or other objects in the underground cavity. This allows you to effectively manage the underground utility tunnel and prevent disasters.

• Land and Housing Review
• /
• v.14 no.3
• /
• pp.145-155
• /
• 2023

Recently, with the widespread adoption of Building Information Modeling (BIM) technology in the construction industry, various object detection algorithms have been used to verify errors between 3D models and actual construction elements. Since the characteristics of objects vary depending on the type of construction facility, such as buildings, bridges, and tunnels, appropriate methods for object detection technology need to be employed. Additionally, for object detection, initial object images are required, and to obtain these, various methods, such as drones and smartphones, can be used for image acquisition. The study uses a 360° camera optimized for internal tunnel imaging to capture initial images of the tunnel structures of railway and road facilities. Various object detection methodologies including the YOLO, SSD, and R-CNN algorithms are applied to detect actual objects from the captured images. And the Faster R-CNN algorithm had a higher recognition rate and mAP value than the SSD and YOLO v5 algorithms, and the difference between the minimum and maximum values of the recognition rates was small, showing equal detection ability. Considering the increasing adoption of BIM in current railway and road construction projects, this research highlights the potential utilization of 360° cameras and object detection methodologies for tunnel facility sections, aiming to expand their application in maintenance.

• Journal of the Korean Geotechnical Society
• /
• v.39 no.7
• /
• pp.31-37
• /
• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

• Journal of Wetlands Research
• /
• v.25 no.4
• /
• pp.267-273
• /
• 2023

Algae are an important component of the ecosystem. However, the excessive growth of cyanobacteria has various harmful effects on river environments, and diatoms affect the management of water supply processes. Algal monitoring is essential for sustainable and efficient algae management. In this study, an object detection model was developed that detects and classifies images of four types of harmful cyanobacteria used for the criteria of the algae alert system, and one diatom, Synedra sp.. You Only Look Once(YOLO) v8, the latest version of the YOLO model, was used for the development of the model. The mean average precision (mAP) of the base model was analyzed as 64.4. Five models were created to increase the diversity of the input images used for model training by performing rotation, magnification, and reduction of original images. Changes in model performance were compared according to the composition of the input images. As a result of the analysis, the model that applied rotation, magnification, and reduction showed the best performance with mAP 86.5. The mAP of the model that only used image rotation, combined rotation and magnification, and combined image rotation and reduction were analyzed as 85.3, 82.3, and 83.8, respectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2019.11a
• /
• pp.80-82
• /
• 2019

드론은 좁은 장소나, 도로 위에서도 자유롭게 운용할 수 있다는 등의 장점으로 인해 점차 교통 모니터링 분야에 서도 널리 쓰이고 있다. 교통 모니터링을 통해 교통관제가 가능하며, 교통혼잡 해소에 활용할 수 있다. 교통량 확인을 위하여 기존에는 hand-crafted 기반의 방법들이 사용되었는데, 이러한 방법들은 조명이나 촬영위치에 취약하다. 따라서 이러한 문제를 해결하기 위해 본 논문에서는 딥러닝 기반의 교통량 확인 알고리즘을 제안하였다. 본 논문에서는 드론의 촬영 환경과 비슷한 환경의 도로 데이터를 수집하였다. 정확도를 좀 더 높이기 위해, 데이터 augmentation 을 하였다. 생성된 데이터를 이용하여 학습을 진행하였고, 학습 결과 97%의 정확도가 나옴을 확인하였다. 테스트 데이터에 대한 정확도 측정은 [250 pixel 이상] X [250 pixel 이상] 크기의 객체에 대해서 IOU 0.3 기준으로 측정되었다.

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

• Journal of Korea Multimedia Society
• /
• v.24 no.2
• /
• pp.215-221
• /
• 2021

The agricultural is plagued by agricultural damage from wild boars every year. As a result, research on systems to repelling wild boars continues, and most of the systems are to detect objects with body temperature through sensors and then repelling them with actions such as light and sound. The problems of these system are operating regardless of wild boars and people, which can cause significant accident when using electric fence. In addition, If the same repelling action is repeated, wild boars can be adapted to that repelling action. As a solution to the two problems, Adaptation problem can be solved by random sounds and distinction problem can be solved by YOLO V4.

• International Journal of Advanced Culture Technology
• /
• v.9 no.1
• /
• pp.190-195
• /
• 2021

Currently, one of the fields leading the 4th industrial revolution is the image recognition field of artificial intelligence, which is showing good results in many fields. In this paper, using is a YOLO V2 model, which is one of the image recognition models, we intend to classify and select into three types according to the characteristics of fruits. To this end, it was designed to proceed the number of iterations of learning 9000 counts based on 640 mandarin image data of 3 classes. For model evaluation, normal, rotten, and unripe mandarin oranges were used based on images. We as a result of the experiment, the accuracy of the learning model was different depending on the number of learning. Normal mandarin oranges showed the highest at 60.5% in 9000 repetition learning, and unripe mandarin oranges also showed the highest at 61.8% in 9000 repetition learning. Lastly, rotten tangerines showed the highest accuracy at 86.0% in 7000 iterations. It will be very helpful if the results of this study are used for fruit farms in rural areas where labor is scarce.