Image segmentation based on histogram of depth and an application in driver distraction detection

Dinh, Tran Hiep; Pham, Minh Trien; Phung, Manh Duong; Nguyen, Due Manh; Hoang, Van Manh; Trần, Quang Vĩnh

doi:10.1109/icarcv.2014.7064437

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…In robotic applications, many vision-based approaches have been proposed to deal with robotic color tracking and image segmentation 31,43,44,57 and employed to solve the defect detection problem. [45][46][47] To be applied in a fully automatic process, the proposed approach must be able to handle the input data with limited supervision and manual adjustment.In this work, a hybrid method is proposed to solve the mentioned problem by combining our automatic peak detection algorithm with image stitching and 3D registration.…”

Section: Steel Crack Detectionmentioning

confidence: 99%

Automated robotic monitoring and inspection of steel structures and bridges

La¹,

Dinh²,

Pham³

et al. 2018

Robotica

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SummaryThis paper presents visual and 3D structure inspection for steel structures and bridges using a developed climbing robot. The robot can move freely on a steel surface, carry sensors, collect data and then send to the ground station in real-time for monitoring as well as further processing. Steel surface image stitching and 3D map building are conducted to provide a current condition of the structure. Also, a computer vision-based method is implemented to detect surface defects on stitched images. The effectiveness of the climbing robot's inspection is tested in multiple circumstances to ensure strong steel adhesion and successful data collection. The detection method was also successfully evaluated on various test images, where steel cracks could be automatically identified, without the requirement of some heuristic reasoning.

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Section: Steel Crack Detectionmentioning

confidence: 99%

Automated robotic monitoring and inspection of steel structures and bridges

La¹,

Dinh²,

Pham³

et al. 2018

Robotica

Self Cite

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show abstract

“…They introduce and solve the problem of Byzantine fault tolerant distributed quickest change detection in both continuous and discrete-time setups. More reviews could be found in [17][18][19][20][21][22][23][24][25].…”

Section: Literature Review and Analysismentioning

confidence: 99%

A Novel Image Change Detection Model and Optimization Algorithm based on Game Theory under the Bounded Rationality Conditions

Wu¹,

Scott²

2016

IJSIA

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“…Compared to traditional methods, the CNN-based crack detection framework does not require the pre-extraction and calculation of features [32]. Moreover, CNN is based on automatic learning of crack features and does not require format conversion of input images [33]. Furthermore, the CNN-based crack detection architecture displays higher accuracy than the traditional methods [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages

Munawar

Ullah

Heravi

et al. 2021

Drones

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Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the union overall classes (IoU) (0.879), precision (0.838), recall (0.879), and F-score (0.8581) values. Overall, the proposed CNN architecture provides the advantages of reduced noise, highly integrated supervision of features, adequate learning, and aggregation of both multi-scale and multilevel features during the training procedure along with the refinement of the overall output predictions.

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Image segmentation based on histogram of depth and an application in driver distraction detection

Cited by 10 publications

References 10 publications

Automated robotic monitoring and inspection of steel structures and bridges

Automated robotic monitoring and inspection of steel structures and bridges

A Novel Image Change Detection Model and Optimization Algorithm based on Game Theory under the Bounded Rationality Conditions

Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages

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