VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T12:22:53ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2023-06-15T04:10:16Z2023-06-15T04:10:16Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4817This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/48172023-06-15T04:10:16ZExtended Upscale and Downscale Representation with Cascade ArrangementSmoothing filters are widely used in EEG signal processing for noise removal while preserving important features. Unlike common approaches in the time domain, a recent effective algorithm using the Upscale and Downscale Representation (UDR) technique has been introduced to process the signal in the image domain. The idea of UDR is to visualize the input with an appropriate line width, convert it to a binary image, and then smooth it by skeletonizing the signal object to a unit width and projecting it back to the time domain. We propose in this paper a cascaded UDR (CUDR) where the interested signal is filtered twice. CUDR’s performance is verified on simulated data with added white Gaussian noise and compared with the cascaded arrangement of some conventional techniques. Experimental results have demonstrated the outperformance of
CUDR in terms of the fitting error when dealing with noisy signals, especially at a low signal-to-noise ratio.Quang Manh Doanmdq@vnu.edu.vnTran Hiep Dinhtranhiep.dinh@vnu.edu.vnLinh Trung Nguyenlinhtrung@vnu.edu.vnDiep Nguyen NDiep.Nguyen@uts.edu.auAvinash Kumar Singhavinash.singh@uts.edu.auChin-Teng Linchin-teng.lin@uts.edu.au2022-08-22T04:06:22Z2022-08-22T04:06:22Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4775This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47752022-08-22T04:06:22ZMasked Face Detection with Illumination AwarenessMask mandate has been applied in many countries in the last two years as a simple but effective way to limit the Covid-19 transmission. Besides the guidance from authorities regarding mask use in public, numerous vision-based approaches have been developed to aid with the monitoring of face mask wearing. Despite promising results have been obtained, several challenges in vision-based masked face detection still remain, primarily due to the insufficient of a quality dataset covering adequate variations in lighting conditions, object scales, mask types, or occlusion levels. In this paper, we investigate the effectiveness of a lightweight masked face detection system under different lighting conditions and the possibility of enhancing its performance with the employment of an image enhancement algorithm and an illumination awareness classifier. A dataset of human subjects with and without face masks in different lighting conditions is first introduced. An illumination awareness classifier is then trained on the collected dataset, the labeling of which is processed automatically based on the difference in detection accuracy when an image enhancement algorithm is taken into account. Experimental results have shown that the combination of the masked face detection system with the illumination awareness
and an image enhancement algorithm can boost the system performance to up to 8.6%, 7.4%, and 8.5% in terms of Accuracy, F1-score, and AP-M, respectively.Tran Hiep Dinhtranhiep.dinh@vnu.edu.vnLinh Trung Nguyenlinhtrung@vnu.edu.vn2021-05-31T10:58:32Z2021-05-31T10:58:32Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4437This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/44372021-05-31T10:58:32ZHierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack DetectionDrone imagery is increasingly used in automated inspection for infrastructure surface defects, especially in hazardous or unreachable environments. In machine vision, the key to crack detection rests with robust and accurate algorithms for image processing. To this end, this paper proposes a deep learning approach using hierarchical convolutional neural networks with feature preservation (HCNNFP) and an intercontrast iterative thresholding algorithm for image binarization. First, a set of branch networks is proposed, wherein the output of previous convolutional blocks is half-sizedly concatenated to the current ones to reduce the obscuration in the down-sampling stage taking into account the overall information loss. Next, to extract the feature map generated from the enhanced HCNN, a binary contrast-based autotuned thresholding (CBAT) approach is developed at the post-processing step, where patterns of interest are clustered within the probability map of the identified features. The proposed technique is then applied to identify surface cracks on the surface of roads, bridges or pavements. An extensive comparison with existing techniques is conducted on various datasets and subject to a number of evaluation criteria including the average F-measure (AFβ) introduced here for dynamic quantification of the performance. Experiments on crack images, including those captured by unmanned aerial vehicles inspecting a monorail bridge. The proposed technique outperforms the existing methods on various tested datasets especially for GAPs dataset with an increase of about 1.4% in terms of AFβ while the mean percentage error drops by 2.2%. Such performance demonstrates the merits of the proposed HCNNFP architecture for surface defect inspection.Qiuchen ZhuQiuchen.Zhu@student.uts.edu.auTran Hiep Dinhtranhiep.dinh@vnu.edu.vnManh Duong Phungduongpm@vnu.edu.vnHa Quangquang.ha@uts.edu.au2020-07-10T05:37:09Z2020-07-10T09:17:18Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3996This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/39962020-07-10T05:37:09ZDefect detection based on singular value decomposition and
histogram thresholdingThis paper presents a novel method for defect
detection based on singular value decomposition (SVD) and
histogram thresholding. First, the input image is divided
into blocks, where SVD is applied to determine if a
region contains crack pixels. The detected crack blocks
are then merged to construct a histogram to calculate
the best binarization threshold by incoporating a recent
technique for multiple peaks detection and Otsu algorithm.
To validate the effectiveness and advantage of the
proposed approach over related thresholding algorithms,
experiments on images collected by an unmanned aerial
vehicle have been conducted for surface crack detection.
The obtained results have confirmed the merits of the
proposed approach in terms of accuracy when using some
well-known evaluation metrics.Xuan Tuyen Tranxuantuyen2901@gmail.comTran Hiep Dinhtranhiep.dinh@vnu.edu.vnVu Ha Lehalv@vnu.edu.vnQiuchen ZhuQiuchen.Zhu@student.uts.edu.auQuang Haquang.ha@uts.edu.au2019-10-01T03:01:48Z2019-10-01T03:01:48Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3566This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/35662019-10-01T03:01:48ZAutomated robotic monitoring and inspection of steel structures and bridgesHung Manh LaTran Hiep Dinhtranhiep.dinh@vnu.edu.vnNhan Huu PhamQuang Phuc HaAnh Quyen Pham2019-09-30T04:26:05Z2019-09-30T04:26:05Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3558This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/35582019-09-30T04:26:05ZCrack Detection Using Enhanced Thresholding on UAV based Collected ImagesThis paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recursively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified criterion of interclass contrast is proposed and employed as an object cost and stop condition for the recursive process. Experiments on different datasets show that our algorithm outperforms different segmentation approaches to accurately extract crack features of some commercial buildings.Qiuchen ZhuQiuchen.Zhu@student.uts.edu.auTran Hiep Dinhtranhiep.dinh@vnu.edu.vnManh Duong Phungduongpm@vnu.edu.vnQuang Ha2019-09-16T02:14:50Z2019-09-16T02:14:50Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3556This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/35562019-09-16T02:14:50ZReconfigurable Multi-UAV Formation Using Angle-Encoded PSOIn this paper, we propose an algorithm for the formation of multiple UAVs used in vision-based inspection of infrastructure. A path planning algorithm is first developed by using a variant of the particle swarm optimisation, named θ-PSO, to generate a feasible path for the overall formation configuration taken into account the constraints for visual inspection. Here, we introduced a cost function that includes various constraints on flight safety and visual inspection. A reconfigurable topology is then added based on the use of intermediate waypoints to allow the formation to avoid collision with obstacles during operation. The planned path and formation are then combined to derive the trajectory and velocity profiles for each UAV. Experiments have been conducted for the task of inspecting a light rail bridge. The results confirmed the validity and effectiveness of the proposed algorithm.Van Truong HoangVanTruong.Hoang@student.uts.edu.auManh Duong Phungduongpm@vnu.edu.vnTran Hiep Dinhtranhiep.dinh@vnu.edu.vnQiuchen ZhuQiuchen.Zhu@student.uts.edu.auHa Quangquang.ha@uts.edu.au2019-08-21T03:35:43Z2019-08-21T03:35:43Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3552This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/35522019-08-21T03:35:43ZSummit Navigator: A Novel Approach for Local Maxima ExtractionThis paper presents a novel method, called the Summit Navigator, to effectively extract local maxima of an image histogram for multi-object segmentation of images. After smoothing with a moving average filter, the obtained histogram is analyzed, based on the data density and distribution to find the best observing location. An observability index for each initial peak is proposed to evaluate if it can be considered as dominant by using the calculated observing location. Recursive algorithms are then developed for peak searching and merging to remove any false detection of peaks that are located on one side of each mode. Experimental results demonstrated the advantages of the proposed approach in terms of accuracy and consistency in different reputable datasets.Tran Hiep Dinhtranhiep.dinh@vnu.edu.vnManh Duong Phungduongpm@vnu.edu.vnQuang Haquang.ha@uts.edu.au2019-08-08T02:38:21Z2019-08-08T02:38:21Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3546This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/35462019-08-08T02:38:21ZMultiple UAV Coordination based on the Internet of Things for Real-time Surface InspectionThis paper presents a real-time control system for surface inspection using multiple unmanned aerial vehicles (UAVs). The UAVs are coordinated in a specific formation to collect data of the inspecting objects. The communication platform for data transmission is based on the Internet of Things (IoT). In the proposed architecture, the UAV formation is established via using the angle-encoded particle swarm optimisation to generate an inspecting path and redistribute it to each UAV where communication links are embedded with an IoT board for network and data processing capabilities. Data collected are transmitted in real time through the network to remote computational units. To detect potential damage or defects, an online image processing technique is proposed and implemented based on histograms. Extensive simulation, experiments and comparisons have been conducted to verify the validity and performance of the proposed system.Van Truong HoangManh Duong Phungduongpm@vnu.edu.vnTran Hiep Dinhtranhiep.dinh@vnu.edu.vnQuang Ha2017-08-03T02:21:47Z2017-08-03T02:21:47Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2564This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/25642017-08-03T02:21:47ZAutomatic Crack Detection in Built Infrastructure Using Unmanned Aerial VehiclesThis paper addresses the problem of automatic inspection by means of Unmanned Aerial Vehicles (UAV) for health monitoring of infrastructure. Our approach comprises two stages, data collection using unmanned aerial vehicles and image processing with histogram analysis. For the data collection, a 3D model of the monitored structure is first created by using vision-based sensors attached on the UAV. Based on the model developed, geometrical properties are extracted to generate way points necessary for navigating the UAV for image capturing of the structure of different materials, for example, concrete. From the images obtained, our next step is to stick them together using the overlapped field of view. We then create histograms of the stuck images and detect peaks based on cosine similarity. We finally identify a potential crack or surface defect as location of the histogram peaks. The whole process is automatically carried out so that the inspection time is significantly improved while minimising any safety hazards that may be encountered in the UAV inspection process. A prototypical system has been developed with obtained results being evaluated and verified to show its validity.Manh Duong Phungduongpm@vnu.edu.vnVan Truong HoangTran Hiep Dinhtranhiep.dinh@vnu.edu.vnQuang Haquang.ha@uts.edu.au2017-06-14T09:57:38Z2017-06-14T09:57:38Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2390This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23902017-06-14T09:57:38ZAutomatic Interpretation of Unordered Point Cloud Data for UAV Navigation in ConstructionThe objective of this work is to develop a data processing system that can automatically generate waypoints for navigation of an unmanned aerial vehicle (UAV) to inspect surfaces of structures like buildings and bridges. The input includes data recorded by two 2D laser scanners, orthogonally mounted on the UAV, and an inertial measurement unit (IMU). To achieve the goal, algorithms are developed to process the data collected. They are separated into three major groups: (i) the data registration and filtering to generate a 3D model of the structure and control the density of point clouds for data completeness enhancement; (ii) the surface and obstacle detection to assist the UAV in monitoring tasks; and (iii) the waypoint generation to set the flight path. Experiments on different data sets show that the developed system is able to reconstruct a 3D point cloud of the structure, extract its surfaces and objects, and generate waypoints for the UAV to accomplish inspection tasks.Manh Duong Phungduongpm@vnu.edu.vnCong Hoang Quachhoangqc@vnu.edu.vnDuc Trinh Chutrinhcd@vnu.edu.vnNgoc Que Nguyenquenn@gmail.comTran Hiep Dinhtranhiep.dinh@vnu.edu.vnQuang Haquangha@gmail.com2017-06-14T09:49:05Z2017-06-14T09:49:05Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2512This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/25122017-06-14T09:49:05ZEnhanced discrete particle swarm optimization path planning for UAV vision-based surface inspectionIn built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge.Manh Duong Phungduongpm@vnu.edu.vnCong Hoang QuachTran Hiep Dinhtranhiep.dinh@vnu.edu.vnHa Quangquang.ha@uts.edu.au2016-09-02T16:05:56Z2016-09-02T16:07:46Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1865This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/18652016-09-02T16:05:56ZImage segmentation based on histogram of depth and an application in driver distraction detectionThis study proposes an approach to segment human object from a depth image based on histogram of depth values. The region of interest is first extracted based on a predefined threshold for histogram regions. A region growing process is then employed to separate multiple human bodies with the same depth interval. Our contribution is the identification of an adaptive growth threshold based on the detected histogram region. To demonstrate the effectiveness of the proposed method, an application in driver distraction detection was introduced. After successfully extracting the driver's position inside the car, we came up with a simple solution to track the driver motion. With the analysis of the difference between initial and current frame, a change of cluster position or depth value in the interested region, which cross the preset threshold, is considered as a distracted activity. The experiment results demonstrated the success of the algorithm in detecting typical distracted driving activities such as using phone for calling or texting, adjusting internal devices and drinking in real time.Tran Hiep Dinhtranhiep.dinh@vnu.edu.vnMinh Trien Phamtrienpm@vnu.edu.vnManh Duong Phungduongpm@vnu.edu.vnDue Manh NguyenVan Manh HoangQuang Vinh Tranvinhtq@vnu.edu.vn2015-12-03T06:55:34Z2015-12-03T07:02:52Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/863This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/8632015-12-03T06:55:34ZTrajectory Tracking Control of the Nonholonomic Mobile Robot using Torque Method and Neural NetworkThuan Hoang TranTran Hiep Dinhhiep.tran.dinh@vnu.edu.vnGia Duong Bachduongbg@vnu.edu.vnQuang Vinh Tranvinhtq@vnu.edu.vn2015-05-22T04:46:49Z2015-11-21T15:48:59Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/865This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/8652015-05-22T04:46:49ZProposal of Algorithms for Navigation and Obstacles Avoidance of Autonomous Mobile RobotThuan Hoang TranTran Hiep Dinhtranhiep.dinh@vnu.edu.vnManh Duong Phungduongpm@vnu.edu.vnThi Thanh Van Nguyenvanntt@vnu.edu.vnGia Duong Bachduongbg@vnu.edu.vnQuang Vinh Tranvinhtq@vnu.edu.vn2013-08-08T06:25:08Z2013-08-08T06:25:08Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/177This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/1772013-08-08T06:25:08ZPhương pháp xác định mặt phẳng nghiêng sử dụng cảm biến siêu âm trong mô hình rửa xe tự độngTran Hiep Dinhtranhiep.dinh@vnu.edu.vnManh Thang Pham thangpm@vnu.edu.vnVan Manh HoangVan Quyen Nguyen Quoc Trung Trinh