• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.338-339
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• 2020

개인 방송의 보편화로 인해 인터넷 혹은 방송으로 유포되는 영상에서 일반인의 얼굴이 빈번히 노출되고 있으며, 동의 받지 않은 얼굴의 방송 노출은 개인 초상권 침해와 같은 사회적 문제를 일으킬 수 있다. 이러한 개인 초상권 침해 문제를 해결하고자 본 논문은 비디오에서 일반인의 얼굴을 검출하고 이에 마스킹을 가하는 방법을 제안한다. 제안 방법은 우선 딥러닝 기반의 Faster R-CNN을 이용하여 모자이킹을 하지 않을 특정인과 모자이킹을 가할 비특정인을 포함한 다수의 얼굴 영상을 학습한다. 학습된 네트워크를 이용하여 입력 비디오에 대해 사람의 얼굴을 검출하고 검출된 결과 중 특정인을 선별해 낸다. 최종적으로 입력 비디오에서 특정인을 제외한 나머지 검출된 얼굴에 대해 모자이킹 처리를 수행함으로써 비디오에서 지능적으로 비특정인의 얼굴을 가린다. 실험결과, 특정인과 비특정인을 포함한 얼굴 검출의 경우 99%의 정확도를 보였으며, 얼굴 검출 결과 중 특정인을 정확히 맞춘 경우는 86%의 정확도를 보였다. 제안 방법은 인터넷 동영상 서비스 및 방송 분야에서 개인 정보 보호를 위해 효과적으로 활용될 수 있을 것으로 기대된다.

• Annual Conference of KIPS
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• 2020.11a
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• pp.251-254
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• 2020

매년 1인 가구를 대상으로 한 범죄가 증가하고 있다. 이에 따라 지문인식, 스마트키와 같은 도어록 제품들이 출시되었지만 오히려 범죄에 악용되는 사례들이 발생하였다. 본 논문에서는 얼굴인식장치(face identifier, FI)를 통해 객체를 인식하고, 원격 도어록 관리자(remote door lock manager, RDM)를 통해 잠금제어부(locking control unit, LCU)를 관리하는 긴급 상황 인식 스마트 도어록을 제안한다. 사용자의 얼굴을 얼마나 빠르고 정확하게 인식하는지 속도와 신뢰도에 대한 테스트를 진행하였고, 긴급 상황 시 사용자가 안전하게 집으로 들어갈 수 있음을 확인하였다. 본 제품을 통해 주거 침입, 스토킹 등 1인 가구 대상 범죄율과 도어록 악용 범죄율이 낮아질 것으로 사료된다.

• Journal of KIISE
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• v.44 no.7
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• pp.668-673
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• 2017

Deep neural networks have shown remarkable performance in various fields of pattern recognition such as voice recognition, image recognition and object detection. However, underlying mechanisms of the network have not been fully revealed. In this paper, we focused on empirical analysis of the network parameters. The Faster R-CNN(region-based convolutional neural network) was used as a baseline network of our work and three important parameters were analyzed: the dropout ratio which prevents the overfitting of the neural network, the size of the anchor boxes and the activation function. We also compared the performance of dropout and batch normalization. The network performed favorably when the dropout ratio was 0.3 and the size of the anchor box had not shown notable relation to the performance of the network. The result showed that batch normalization can't entirely substitute the dropout method. The used leaky ReLU(rectified linear unit) with a negative domain slope of 0.02 showed comparably good performance.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.397-408
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• 2021

The construction industry has the highest occupational accidents/injuries and has experienced the most fatalities among entire industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. A long-time monitoring surveillance system causes high physical fatigue and has limitations in grasping all accidents in real-time. Therefore, this study aims to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple object tracking with instance segmentation. To evaluate the system's performance, we utilized the Microsoft common objects in context and the multiple object tracking challenge metrics. These results prove that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.635-644
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• 2020

Object recognition, detection and instance segmentation based on DL (Deep Learning) have being used in various practices, and mainly optical images are used as training data for DL models. The major objective of this paper is object segmentation and building detection by utilizing multimodal datasets as well as optical images for training Detectron2 model that is one of the improved R-CNN (Region-based Convolutional Neural Network). For the implementation, infrared aerial images, LiDAR data, and edges from the images, and Haralick features, that are representing statistical texture information, from LiDAR (Light Detection And Ranging) data were generated. The performance of the DL models depends on not only on the amount and characteristics of the training data, but also on the fusion method especially for the multimodal data. The results of segmenting objects and detecting buildings by applying hybrid fusion - which is a mixed method of early fusion and late fusion - results in a 32.65% improvement in building detection rate compared to training by optical image only. The experiments demonstrated complementary effect of the training multimodal data having unique characteristics and fusion strategy.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.115-127
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• 2023

Video stabilization is one of the camera technologies that the importance is gradually increasing as the personal media market has recently become huge. For deep learning-based video stabilization, existing methods collect pairs of video datas before and after stabilization, but it takes a lot of time and effort to create synchronized datas. Recently, to solve this problem, unsupervised learning method using only unstable video data has been proposed. In this paper, we propose a network structure that learns the stabilized trajectory only with the unstable video image without the pair of unstable and stable video pair using the Convolutional Auto Encoder structure, one of the unsupervised learning methods. Optical flow data is used as network input and output, and optical flow data was mapped into grid units to simplify the network and minimize noise. In addition, to generate a stabilized trajectory with an unsupervised learning method, we define the loss function that smoothing the input optical flow data. And through comparison of the results, we confirmed that the network is learned as intended by the loss function.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.117-117
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• 2022

범지구적인 기후변화로 인하여 도시유역의 국지성 집중호우가 빈번히 발생하고 기상이변 현상이 빈번하게 발생하고 있다. 이로 인해 도시지역의 침수 등의 자연재해 증가로 인명 및 재산피해가 발생하고 있다. 이에 따라 하수도의 제 기능을 수행하고 있다면 문제가 없지만 이상기후로 인한 기록적인 폭우에 의해 침수가 발생하고 있다. 홍수 및 집중호우와 같은 극치사상의 발생빈도가 증가됨에 따라 강우사상의 변동에 따른 하수관로의 수위를 예측하고 침수에 대해 대처하기 위해 과거 수위에 따른 수위 예측은 중요할 것으로 판단된다. 본 연구에서는 서울 열린데이터 광장에서 제공하는 서울시 하수관로 수위 현황 자료를 활용하여 하수관로 수위 예측을 확인해 보았다. 대상자료는 서울특별시 강동구에 위치한 하수관로 수위 자료로, 서울 열린데이터 광장에서 제공하고 있는 2012년 ~ 2020년 25개 구 데이터 중 가장 누락데이터가 적은 자료를 활용하여 연구를 진행하였다. 하수관로 수위 예측에는 딥러닝 알고리즘RNN-LSTM 알고리즘을 활용하였으며, RNN-LSTM 알고리즘은 하천의 수위 예측에 우수한 성능을 보여준 바 있다. 하수관로 수위 예측에 앞서 1분 단위로 수집된 수위 데이터를 5분 평균, 5분 스킵자료, 10분 평균, 10분 스킵 등 비교를 위해 데이터를 구분하여 학습에 활용하였으며, 데이터 분석을 위해 하수관로 수위값 변동이 심한 1주일을 선정하여 분석을 실시하였다. 연구에는Google에서 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하였으며, 하수관로 수위 고유번호 25-0001을 대상으로 예측을 하였다. 학습에는 2012년 ~ 2018년의 하수관로 수위 자료를 활용하였으며, 모형의 검증을 위해 결정계수(R square)를 이용하여 통계분석을 실시하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.