The Fast Prefix Coding Algorithm (FPCA) for 3D Pavement Surface Data Compression

Zhang, Allen; Wang, Kelvin C. P.

doi:10.1111/mice.12243

Cited by 15 publications

(6 citation statements)

References 21 publications

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“…Deep CNN, as one kind of DNN, has presented its great potential for multiscale and multilevel image processing. First, both gray and colorful images, even Red‐Green‐Blue (RGB)‐depth images could be mixed as the input of DNNs (Jahanshahi et al., ; Jahanshahi and Masri, ; Ouyang and Xu, ; Huang et al., ; Jiang and Tsai, ; Zhang et al., ; Guldur Erkal and Hajjar, ; Zhang and Wang, ). On the other side, both local and global features could be learned by DNNs, leading to multilevel outputs, including image level, grid level, and pixel level.…”

Section: Related Researchmentioning

confidence: 99%

Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

Yang

Luo

et al. 2018

Computer aided Civil Eng

587

317

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The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures. However, the current manual crack description method is time consuming and labor consuming. To improve the efficiency of crack inspection, advanced computer vision‐based techniques have been utilized to detect cracks automatically at image level and grid‐cell level. But existing crack detections are of (high specificity) low generality and inefficient, in terms that conventional approaches are unable to identify and measure diverse cracks concurrently at pixel level. Therefore, this research implements a novel deep learning technique named fully convolutional network (FCN) to address this problem. First, FCN is trained by feeding multiple types of cracks to semantically identify and segment pixel‐wise cracks at different scales. Then, the predicted crack segmentations are represented by single‐pixel width skeletons to quantitatively measure the morphological features of cracks, providing valuable crack indicators for assessment in practice, such as crack topology, crack length, max width, and mean width. To validate the prediction, the predicted segmentations are compared with recent advanced method for crack recognition and ground truth. For crack segmentation, the accuracy, precision, recall, and F1 score are 97.96%, 81.73%, 78.97%, and 79.95%, respectively. For crack length, the relative measurement error varies from −48.03% to 177.79%, meanwhile that ranges from −13.27% to 24.01% for crack width. The results show that FCN is feasible and sufficient for crack identification and measurement. Although the accuracy is not as high as CrackNet because of three types of errors, the prediction has been increased to pixel level and the training time has been dramatically decreased to several per cents of previous methods due to the novel end‐to‐end structure of FCN, which combines typical convolutional neural networks and deconvolutional layers.

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Section: Related Researchmentioning

confidence: 99%

Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

Yang

Luo

et al. 2018

Computer aided Civil Eng

587

317

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“…Adeli and Kumar (1999) developed algorithms using parallel and distributed computing techniques that take advantage of the concurrency of multiple processors to execute processes at the same time. Zhang and Wang (2017) used parallel computing techniques in the process of encoding full-lane width pavement in 3D and at 1 mm resolution with an up-to-date desktop computer at 150 MPH and even higher speed. Yu et al (2018) harnessed the parallel computing to optimize the method for prediction of bus arrival time and applied it to real-time prediction that achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Online estimation model for passenger flow state in urban rail transit using multi‐source data

Tao¹,

Tang

Hou

2021

Computer aided Civil Eng

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The estimation of present passenger flow state plays a vital role in the urban rail transit (URT) operation process and it is the basis of passenger flow control and train dispatching. Most of the existing researches used the Automatic Fare Collection (AFC) System data as the single data source for estimating present passenger flow state. In consideration of the delay in the data uploading process, the obtained estimation results based on the single AFC data source are not accurate enough, which could lead to the improper handling and decision failure and cause high risk and low efficiency for the operation and management of URT. To address problems mentioned above, this paper adopted the automatic differentiation method as the framework for the fusion of multi-source heterogeneous data (including uploaded AFC data, the mobile phone signaling data provided by mobile phones, and the historical passenger flow data, etc.). Then, this paper proposed the online estimation model and the error layered optimization algorithm to implement the estimation of present passenger flow state. The calculation results indicate that the proposed model and algorithm can obtain reasonable and reliable URT passenger flow state. Finally, the study developed a present passenger flow state estimation system using the proposed model and algorithm. The system has been deployed in Chengdu Metro that serves about 3 million passengers per day from October 2018 to April 2020, with the overall error within 3% as of now.

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“…Recently, Zhang and Wang. [8] proposed a lossless algorithm to compress 3D surfaces and showed that the compression ratio can be even higher than 100 for smooth 3D surfaces.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition

et al. 2020

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In this paper, we evaluate the effect of scale analysis as well as the filtering process on the performances of an original compressed-domain classifier in the field of material surface topographies classification. Each surface profile is multiscale analyzed by using a Gaussian Filter analyzing method to be decomposed into three multiscale filtered image types: Low-pass (LP), Band-pass (BP), and High-pass (HP) filtered versions, respectively. The complete set of filtered image data constitutes the collected database. First, the images are lossless compressed using the state-of-the art High-efficiency video coding (HEVC) video coding standard. Then, the Intra-Prediction Modes Histogram (IPHM) feature descriptor is computed directly in the compressed domain from each HEVC compressed image. Finally, we apply the IPHM feature descriptors as an input of a Support Vector Machine (SVM) classifier. SVM is introduced here to strengthen the performances of the proposed classification system thanks to the powerful properties of machine learning tools. We evaluate the proposed solution we called “HEVC Multiscale Decomposition” (HEVC-MD) on a huge database of nearly 42,000 multiscale topographic images. A simple preliminary version of the algorithm reaches an accuracy of 52%. We increase this accuracy to 70% by using the multiscale analysis of the high-frequency range HP filtered image data sets. Finally, we verify that considering only the highest-scale analysis of low-frequency range LP was more appropriate for classifying our six surface topographies with an accuracy of up to 81%. To compare these new topographical descriptors to those conventionally used, SVM is applied on a set of 34 roughness parameters defined on the International Standard GPS ISO 25178 (Geometrical Product Specification), and one obtains accuracies of 38%, 52%, 65%, and 57% respectively for Sa, multiscale Sa, 34 roughness parameters, and multiscale ones. Compared to conventional roughness descriptors, the HEVC-MD descriptors increase surfaces discrimination from 65% to 81%.

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The Fast Prefix Coding Algorithm (FPCA) for 3D Pavement Surface Data Compression

Cited by 15 publications

References 21 publications

Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

Online estimation model for passenger flow state in urban rail transit using multi‐source data

A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition

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