VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T13:40:19ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2021-05-31T10:58:32Z2021-05-31T10:58:32Zhttp://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.au2019-12-19T03:17:26Z2019-12-19T03:17:26Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3850This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/38502019-12-19T03:17:26ZCrack Detection Using Enhanced Hierarchical Convolutional Neural NetworksUnmanned aerial vehicles (UAV) are expected to replace human in hazardous tasks of surface inspection due to their flexibility in operating space and capability of collecting high quality visual data. In this study, we propose enhanced hierarchical convolutional neural networks (HCNN) to detect cracks from image data collected by UAVs. Unlike traditional HCNN, here a set of branch networks is utilised to reduce the obscuration in the down-sampling process. Moreover, the feature preserving blocks combine the current and previous terms from the convolutional blocks to provide input to the loss functions in order to reduce the weight of resized images and thus minimise the information loss. Experiments on images of different crack datasets have been carried out to demonstrate the effectiveness of the proposed HCNN.Qiuchen ZhuManh Duong Phungduongpm@vnu.edu.vnHa Quangquang.ha@uts.edu.au2019-09-30T04:26:05Z2019-09-30T04:26:05Zhttp://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.au