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      • KCI등재

        Pavement Crack Detection and Segmentation Based on Deep Neural Network

        Huy Toan Nguyen,Gwang Hyun Yu,Seung You Na,Jin Young Kim,Kyung Sik Seo 한국정보기술학회 2019 한국정보기술학회논문지 Vol.17 No.9

        Cracks on pavement surfaces are critical signs and symptoms of the degradation of pavement structures. Image-based pavement crack detection is a challenging problem due to the intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. In this paper, we address the problem of pavement crack detection and segmentation at pixel-level based on a Deep Neural Network (DNN) using gray-scale images. We propose a novel DNN architecture which contains a modified U-net network and a high-level features network. An important contribution of this work is the combination of these networks afforded through the fusion layer. To the best of our knowledge, this is the first paper introducing this combination for pavement crack segmentation and detection problem. The system performance of crack detection and segmentation is enhanced dramatically by using our novel architecture. We thoroughly implement and evaluate our proposed system on two open data sets: the Crack Forest Dataset (CFD) and the AigleRN dataset. Experimental results demonstrate that our system outperforms eight state-of-the-art methods on the same data sets.

      • KCI등재

        Real-time Moving Object Detection Based on RPCA via GD for FMCW Radar

        Huy Toan Nguyen,Gwang Hyun Yu,Seung You Na,Jin Young Kim,Kyung Sik Seo 한국정보기술학회 2019 한국정보기술학회논문지 Vol.17 No.6

        Moving-target detection using frequency-modulated continuous-wave (FMCW) radar systems has recently attracted attention. Detection tasks are more challenging with noise resulting from signals reflected from strong static objects or small moving objects(clutter) within radar range. Robust Principal Component Analysis (RPCA) approach for FMCW radar to detect moving objects in noisy environments is employed in this paper. In detail, compensation and calibration are first applied to raw input signals. Then, RPCA via Gradient Descents (RPCA-GD) is adopted to model the low-rank noisy background. A novel update algorithm for RPCA is proposed to reduce the computation cost. Finally, moving-targets are localized using an Automatic Multiscale-based Peak Detection (AMPD) method. All processing steps are based on a sliding window approach. The proposed scheme shows impressive results in both processing time and accuracy in comparison to other RPCA-based approaches on various experimental scenarios.

      • KCI등재

        Prediction of Dangerous Moving Vehicles in Blind Spots for a Cyclist Using a Helmet-Mounted Camera

        Huy Toan Nguyen,Seung You Na,Jin Young Kim 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.8

        One of the main causes of traffic collisions for cyclists is incoming vehicles in blind spots. In this paper, we present a helmet-mounted camera system that can identify moving objects which are considered potentially dangerous for cyclists. We first compute the road areas and allocate the positions of vehicles in the image sequences based on the road properties, the Haar-like features, and AdaBoost cascade classifier. Then, we introduce a new algorithm based on ORB features matching to evaluate the probability of collision with the cyclist. A prompt warning sign will show up if a collision is classified as the most likely. Several experiments were conducted in crowded traffic areas under different weather conditions. The experimental results show that our algorithm is highly effective in terms of predicting the danger due to moving vehicles in blind spots and can be processed in real-time.

      • Combining Fuzzy C-means Clustering and Flood Filling Algorithm for Enhancing Text Binarization

        Huy Phat Le,Toan Dinh Nguyen,Jonghyun Park,GuessSang Lee 한국멀티미디어학회 2009 한국멀티미디어학회 학술발표논문집 Vol.2009 No.1

        Text binarization is an important step in text understanding due to the fact that ORC (Optical Character Recognition) system only understands the binarized image. The more accurate the binarized text is; the better result the ORC system works. In this paper, a novel binarization method is proposed to binarize text from complex color images. First, the Fuzzy C-means algorithm is used to group similar pixels color in an image. The flood filling algorithm is then used to remove noise components in the background. Finally, we used Otsu global binarization method to binarize the text image. The experimental results show that our method outperforms the Otsu global binarization method.

      • KCI등재

        Label Restoration Using Biquadratic Transformation

        Huy Phat Le,Toan Dinh Nguyen,GueeSang Lee 한국콘텐츠학회(IJOC) 2010 International Journal of Contents Vol.6 No.1

        Recently, there has been research to use portable digital camera to recognize objects in natural scene images, including labels or marks on a cylindrical surface. In many cases, text or logo in a label can be distorted by a structural movement of the object on which the label resides. Since the distortion in the label can degrade the performance of object recognition, the label should be rectified or restored from deformations. In this paper, a new method for label detection and restoration in digital images is presented. In the detection phase, the Hough transform is employed to detect two vertical boundaries of the label, and a horizontal edge profile is analyzed to detect upper-side and lower-side boundaries of the label. Then, the biquadratic transformation is used to restore the rectangular shape of the label. The proposed algorithm performs restoration of 3D objects in a 2D space, and it requires neither an auxiliary hardware such as 3D camera to construct 3D models nor a multi-camera to capture objects in different views. Experimental results demonstrate the effectiveness of the proposed method.

      • KCI등재

        Label Restoration Using Biquadratic Transformation

        Le, Huy Phat,Nguyen, Toan Dinh,Lee, Guee-Sang The Korea Contents Association 2010 International Journal of Contents Vol.6 No.1

        Recently, there has been research to use portable digital camera to recognize objects in natural scene images, including labels or marks on a cylindrical surface. In many cases, text or logo in a label can be distorted by a structural movement of the object on which the label resides. Since the distortion in the label can degrade the performance of object recognition, the label should be rectified or restored from deformations. In this paper, a new method for label detection and restoration in digital images is presented. In the detection phase, the Hough transform is employed to detect two vertical boundaries of the label, and a horizontal edge profile is analyzed to detect upper-side and lower-side boundaries of the label. Then, the biquadratic transformation is used to restore the rectangular shape of the label. The proposed algorithm performs restoration of 3D objects in a 2D space, and it requires neither an auxiliary hardware such as 3D camera to construct 3D models nor a multi-camera to capture objects in different views. Experimental results demonstrate the effectiveness of the proposed method.

      • KCI등재

        Analyze weeds classification with visual explanation based on Convolutional Neural Networks

        Vo, Hoang-Trong,Yu, Gwang-Hyun,Nguyen, Huy-Toan,Lee, Ju-Hwan,Dang, Thanh-Vu,Kim, Jin-Young THE KOREAN INSTITUTE OF SMART MEDIA 2019 스마트미디어저널 Vol.8 No.3

        To understand how a Convolutional Neural Network (CNN) model captures the features of a pattern to determine which class it belongs to, in this paper, we use Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize and analyze how well a CNN model behave on the CNU weeds dataset. We apply this technique to Resnet model and figure out which features this model captures to determine a specific class, what makes the model get a correct/wrong classification, and how those wrong label images can cause a negative effect to a CNN model during the training process. In the experiment, Grad-CAM highlights the important regions of weeds, depending on the patterns learned by Resnet, such as the lobe and limb on 미국가막사리, or the entire leaf surface on 단풍잎돼지풀. Besides, Grad-CAM points out a CNN model can localize the object even though it is trained only for the classification problem.

      • Appropriation of Protocol for Laparoscopic Pancreaticoduodenectomy in Treatment of Periampullary Cancer

        ( Minh Hai Pham ),( Quan Anh Tuan Le ),( Hoang Bac Nguyen ),( Quang Hung Vu ),( Thai Ngoc Huy Tran ),( Hang Dang Khoa N Guyen ),( Thi Ngoc Sang Duong ),( Van Toan Tran ) 대한간학회 2020 춘·추계 학술대회 (KASL) Vol.2020 No.1

        Aims: Laparoscopic pancreaticoduodenectomy (LPD) is considered as a safe and effective procedure in well - selected patients and appropriate surgical technique. Our aim is to evaluate suitability of using protocol for LPD in treatment of periampullary cancer at a single team. Methods: case series Results: Indication for LPD included 37 cases with resectable tumors which were classified basing on NCCN. All witness evaluation risk of complications with PREPARE score, ASA and evaluation risk of postoperative pancreatic fistula (POPF) with FRS classification. There were 2 open conversions because of vein resections, accounting for 5.4%. Standard lymphadenectomy was performed in all of 37 cases. In term of PREPARE score, major complications (Clavien - Dindo >= III) were 17.8%, 0% and 0% (5/28, 0/5 and 0/2 cases) in low risk, intermediate risk and high risk group, respectively. All of cases had ASA I or II. POPF happened 11.1% (1/9), 4.1% (1/24) and 50% (1/2) in low risk, intermediate risk and high risk group, respectively. Frozen section was needed for R0 margin. Retrieved lymph nodes was 8 - 18 with 12 lymph nodes in average. Conclusions: Indication for LPD with resectable tumors is acceptable. ASA I or II is a safe measure to select patient for LPD. FRS classification shows appropriation to evaluate risk of POPF.

      • KCI등재

        A Study on Weeds Retrieval based on Deep Neural Network Classification Model

        Vo Hoang Trong,Gwang-Hyun Yu,Dang Thanh Vu,Ju-Hwan Lee,Nguyen Huy Toan,Jin-Young Kim 한국정보기술학회 2020 한국정보기술학회논문지 Vol.18 No.8

        In this paper, we study the ability of content-based image retrieval by extracting descriptors from a deep neural network (DNN) trained for classification purposes. We fine-tuned the VGG model for the weeds classification task. Then, the feature vector, also a descriptor of the image, is obtained from a global average pooling (GAP) and two fully connected (FC) layers of the VGG model. We apply the principal component analysis (PCA) and develop an autoencoder network to reduce the dimension of descriptors to 32, 64, 128, and 256 dimensions. We experiment weeds species retrieval problem on the Chonnam National University (CNU) weeds dataset. The experiment shows that collecting features from DNN trained for weeds classification task can perform well on image retrieval. Without applying dimensionality reduction techniques, we get 0.97693 on the mean average precision (mAP) value. Using autoencoder to reduced dimensional descriptors, we achieve 0.97719 mAP with the descriptor dimension is 256.

      • KCI등재

        A Study on Applying the SRCNN Model and Bicubic Interpolation to Enhance Low-Resolution Weeds Images for Weeds Classification

        Vo Hoang Trong,Yu Gwang-hyun,Dang Thanh Vu,Lee Ju-hwan,Nguyen Huy Toan,Kim Jin-young 한국스마트미디어학회 2020 스마트미디어저널 Vol.9 No.4

        In the image object classification problem, low-resolution images may have a negative impact on the classification result, especially when the classification method, such as a convolutional neural network (CNN) model, is trained on a high-resolution (HR) image dataset. In this paper, we analyze the behavior of applying a classical super-resolution (SR) method such as bicubic interpolation, and a deep CNN model such as SRCNN to enhance low-resolution (LR) weeds images used for classification. Using an HR dataset, we first train a CNN model for weeds image classification with a default input size of 128×128. Then, given an LR weeds image, we rescale to default input size by applying the bicubic interpolation or the SRCNN model. We analyze these two approaches on the Chonnam National University (CNU) weeds dataset and find that SRCNN is suitable for the image size is smaller than 80×80, while bicubic interpolation is convenient for a larger image.

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