• Smart Structures and Systems
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• v.29 no.1
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.485-487
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• 2023

컴퓨터 비전의 하위 태스크(Task)인 의미론적 분할(Semantic Segmentation)은 자율주행, 해상에서 선박찾기 등 다양한 분야에서 연구되고 있다. 기존 FCN(Fully Conovlutional Networks) 기반 의미론적 분할 모델은 다운샘플링(Dowsnsampling)과정에서 공간정보의 손실이 발생하여 정확도가 하락했다. 본 논문에서는 공간정보 손실을 완화하고자 PSA(Polarized Self-attention)의 공간정보 강조 모듈을 HRNet(High-resolution Networks)의 합성곱 블록 사이에 추가한다. 실험결과 파라미터는 3.1M, GFLOPs는 3.2G 증가했으나 mIoU는 0.26% 증가했다. 공간정보가 의미론적 분할 정확도에 영향이 미치는 것을 확인했다.

• Journal of radiological science and technology
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• v.47 no.1
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• pp.29-37
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• 2024

This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.33-42
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• 2022

In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.

• Electronics and Telecommunications Trends
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• v.30 no.3
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• pp.84-94
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• 2015

5G와 IoT로 인한 무선 트래픽 급증에 대응하기 위해서는, 무선망 성능 및 주파수효율 향상, 신규주파수할당, 주파수 공동사용 기술 개발 등이 복합적으로 적용되어야 달성 가능하다. 그의 일환으로, LTE를 비면허 대역에 사용하려는 LTE-U(Long Term Evolution-Unlicensed)라는 새로운 패러다임의 수평적 주파수 공동사용 기술을, 이동통신에 적용하려는 시도가 진행되고 있다. LTE-A의 주파수 집성기술을 활용하여, 1차 캐리어를 면허 대역 LTE 기반으로 하고, 2차 캐리어를 비면허 대역 LTE로 묶어서 고속으로 데이터를 전송하는 기술이다. 우선적으로 5GHz 비면허 대역에 적용을 검토하고 있는데, 기존에 사용하던 Wi-Fi 및 기상레이다 등과의 공정한 공존(fair coexistence)이 가장 중요하다. 따라서 각국의 5GHz 주파수 대역 규제 현황, 공존을 위한 LBT(Listen-Before-Talk)통신 메커니즘, 표준화 현황을 살펴본다. 또한 이해 당사자인 Wi-Fi, LTE, 이용자, 기술기준의 입장을 살펴보고, 구현이슈, 지적소유권 동향 등을 검토하고, 기술적 및 정책적 대응전략을 살펴본다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3364-3382
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• 2023

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC, mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.438-440
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• 2022

산업 현장에서 인력으로 작업할 수 있는 물리적 한계를 극복하기 위해 특수 목적 차량 작업 시 차량의 넘어짐 방지와 차체 보호를 위해 아웃트리거를 착지시키는데 도로 상태에 따라 사용자가 직접 최적화를 수행하는데 어려움이 존재한다. 본 논문에서는 도로 표면 상태를 신속하게 판단하여 아웃트리거 수직 및 수평 전개 착지 시 시간 소모, 안전사고 발생을 낮추기 위해 시멘틱 분할을 이용한 도로 표면 상태를 분석하는 연구를 수행하였다. 13가지로 구분된 도로 표면 상황에 대하여 DeepLabV3+를 통해 실험한 결과 픽셀 성능0.7819, mIoU 0.7085 결과를 도출하였다.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.92-102
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• 2018

On a global level, the energy problem is a very important policy topic, particularly at a time when the nation relies on imports for more than 95% of its energy demand. The starting point of an energy policy should be in line with the international community's concern and cooperation regarding climate warming, and the logic of the new policy on renewable energy expansion in Korea, the pre-developed energy sector, and policy of deserting coal all support this aspect. In particular, to accommodate the rapid urbanization of mankind, the key words of the 4th Industrial Revolution are linking energy to IoT, artificial intelligence, block chain, cloud, and big data.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.637-639
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• 2019

최근 관광산업은 AI, 빅데이터, IoT, 증강현실 등 4 차 산업혁명 관련 기술이 활용을 활용하여 관광산업 활성화를 도모하고 있다. 모바일 애플리케이션을 통한 개인 맞춤형 서비스가 다수 개발되고 있으나, 낙후된 지역사회 관광지에는 아직까지 테마로드 같은 콘텐츠 개발이 미비한 상황이다. 이에 본 연구에서는 QR 코드, 블루투스, 비콘 등의 기술을 기반으로 사용자가 쉽게 이용할 수 있는 위치 기반 서비스 알고리즘을 개발하고자 하며 이를 통해 침체된 구도시의 관광객 유치와 관람객들이 재미있게 활용할 수 있는 테마로드 콘텐츠를 제공하고자 한다.