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Book ; Online: Development of Real-time Rendering Technology for High-Precision Models in Autonomous Driving

Whencheng, Zhang / Chengyi, Wang

2023

Abstract: Our autonomous driving simulation lab produces a high-precision 3D model simulating the parking lot. However, the current model still has poor rendering quality in some aspects. In this work, we develop a system to improve the rendering of the model and ... ...

Abstract	Our autonomous driving simulation lab produces a high-precision 3D model simulating the parking lot. However, the current model still has poor rendering quality in some aspects. In this work, we develop a system to improve the rendering of the model and evaluate the quality of the rendered model. Comment: 3 pages, 6 figures
Keywords	Computer Science - Robotics ; Computer Science - Computer Vision and Pattern Recognition
Publishing date	2023-02-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery

Yi Zhang / Chengyi Wang / Jingbo Chen / Futao Wang

Remote Sensing, Vol 14, Iss 1249, p

2022 Volume 1249

Abstract: Large-scale and periodic remote sensing monitoring of marine raft aquaculture areas is significant for scientific planning of their layout and for promoting sustainable development of marine ecology. Synthetic aperture radar (SAR) is an important tool ... ...

Abstract	Large-scale and periodic remote sensing monitoring of marine raft aquaculture areas is significant for scientific planning of their layout and for promoting sustainable development of marine ecology. Synthetic aperture radar (SAR) is an important tool for stable monitoring of marine raft aquaculture areas since it is all-weather, all-day, and cloud-penetrating. However, the scattering signal of marine raft aquaculture areas is affected by speckle noise and sea state, so their features in SAR images are complex. Thus, it is challenging to extract marine raft aquaculture areas from SAR images. In this paper, we propose a method to extract marine raft aquaculture areas from Sentinel-1 images based on the analysis of the features for marine raft aquaculture areas. First, the data are preprocessed using multitemporal phase synthesis to weaken the noise interference, enhance the signal of marine raft aquaculture areas, and improve the significance of the characteristics of raft aquaculture areas. Second, the geometric features of the marine raft aquaculture area are combined to design the model structure and introduce the shape constraint module, which adds a priori knowledge to guide the model convergence direction during the training process. Experiments verify that the method outperforms the popular semantic segmentation model with an F 1 of 84.52%.
Keywords	monitoring of mariculture ; SAR ; image synthesis ; semantic segmentation ; Science ; Q
Subject code	333
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation

Chengyi Wang / Lianfa Li

Remote Sensing, Vol 12, Iss 2932, p

2020 Volume 2932

Abstract: It is challenging for semantic segmentation of buildings based on high-resolution remote sensing images, given high variability of appearance and complicated backgrounds of the buildings and their images. In this communication, we proposed an ensemble ... ...

Abstract	It is challenging for semantic segmentation of buildings based on high-resolution remote sensing images, given high variability of appearance and complicated backgrounds of the buildings and their images. In this communication, we proposed an ensemble multi-scale residual deep learning method with the regularizer of shape representation for semantic segmentation of buildings. Based on the U-Net architecture using residual connections and multi-scale ASPP (atrous spatial pyramid pooling) modules, our method introduced the regularizer of shape representation and ensemble learning of multi-scale models to enhance model training and reduce over-fitting. In our method, the shape representation was coded in an antoencoder that was used to encode and reconstruct the shape characteristics of the buildings. In prediction, we consider multi-scale trained models for different resolution inputs and side effects to obtain an optimal semantic segmentation. With the high-resolution image of the Changshan, an island county in China, we used two-thirds of the study region image to train the model and the remaining one-third for the independent test. We obtained the accuracy of 0.98–0.99, mean intersection over union (MIoU) of 0.91–0.93 and Jaccard coefficient of 0.89–0.92 in validation. In the independent test, our method achieved state-of-the-art performance (MIoU: 0.83; Jaccard index: 0.81). By comparing with the existing representative methods on four different data sets, the proposed method consistently improved the learning process and generalization. The study shows important contributions of ensemble learning of multi-scale residual models and regularizer of shape representation to semantic segmentation of buildings.
Keywords	multiple scales ; residual deep ensemble learning ; regularizer ; shape representation ; semantic segmentation of buildings ; Science ; Q
Subject code	006
Language	English
Publishing date	2020-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Segmentation and recognition of breast ultrasound images based on an expanded U-Net.

Yanjun Guo / Xingguang Duan / Chengyi Wang / Huiqin Guo

PLoS ONE, Vol 16, Iss 6, p e

2021 Volume 0253202

Abstract: This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application ... ...

Abstract	This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application scenarios of semantic segmentation of breast ultrasound images, and adds dropout layers to the U-Net architecture to reduce the redundancy in texture details and prevent overfitting. The main innovation of this paper is proposing an expanded training approach to obtain an expanded of U-Net. The output map of the expanded U-Net can retain texture details and edge features of breast tumours. Using the grey-level probability labels to train the U-Net is faster than using ordinary labels. The average Dice coefficient (standard deviation) and the average IOU coefficient (standard deviation) are 90.5% (±0.02) and 82.7% (±0.02), respectively, when using the expanded training approach. The Dice coefficient of the expanded U-Net is 7.6 larger than that of a general U-Net, and the IOU coefficient of the expanded U-Net is 11 larger than that of the general U-Net. The context of breast ultrasound images can be extracted, and texture details and edge features of tumours can be retained by the expanded U-Net. Using an expanded U-Net can quickly and automatically achieve precise segmentation and multi-class recognition of breast ultrasound images.
Keywords	Medicine ; R ; Science ; Q
Subject code	004
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Coarse-to-Fine Image Registration for Multi-Temporal High Resolution Remote Sensing Based on a Low-Rank Constraint

Peijing Zhang / Xiaoyan Luo / Yan Ma / Chengyi Wang / Wei Wang / Xu Qian

Remote Sensing, Vol 14, Iss 573, p

2022 Volume 573

Abstract: For multi-temporal high resolution remote sensing images, the image registration is important but difficult due to the high resolution and low-stability land-cover. Especially, the changing of land-cover, solar altitude angle, radiation intensity, and ... ...

Abstract	For multi-temporal high resolution remote sensing images, the image registration is important but difficult due to the high resolution and low-stability land-cover. Especially, the changing of land-cover, solar altitude angle, radiation intensity, and terrain fluctuation distortion in the overlapping areas can represent different image characteristics. These time-varying properties cause traditional registration methods with known reference information to fault. Therefore, in this paper we propose a comprehensive coarse-to-fine registration (CCFR) algorithm. First, we design a low-rank constraint-based batch reference extraction (LRC-BRE) method. Under the low-rank constraint, the stable features with highly spatial co-occurrence can be reconstructed via matrix decomposition, and are set as reference images to batch registration. Second, we improve the general feature registration with block feature matching and local linear transformation (BFM-LLT) operators including match outlier filtering (MOF) on regional mutual information and dual-weighted block fitting (DWBF). Finally, based on combining LRC-BRE and BFM-LLT, CCFR is integrated. Experimental results show that the proposed method has a good batch alignment effect, especially in the registration of large difference image pairs. The proposed CCFR achieves a significant performance improvement over many state-of-the-art registration algorithms.
Keywords	registration ; multi-temporal remote sensing images ; high resolution ; low-rank matrix factorization ; optical satellite ; Science ; Q
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Extraction of Floating Raft Aquaculture Areas from Sentinel-1 SAR Images by a Dense Residual U-Net Model with Pre-Trained Resnet34 as the Encoder

Long Gao / Chengyi Wang / Kai Liu / Shaohui Chen / Guannan Dong / Hongbo Su

Remote Sensing, Vol 14, Iss 13, p

2022 Volume 3003

Abstract: Marine floating raft aquaculture (FRA) monitoring is significant for marine ecological environment and food security assessment. Synthetic aperture radar-based monitoring is considered to be an effective means of FRA identification because of its ... ...

Abstract	Marine floating raft aquaculture (FRA) monitoring is significant for marine ecological environment and food security assessment. Synthetic aperture radar-based monitoring is considered to be an effective means of FRA identification because of its capability for all-weather applications. Considering the poor generalization and extraction accuracy of traditional monitoring methods, a semantic segmentation model called D-ResUnet is proposed to extract FRA areas from Sentinel-1 images. The proposed model has a U-Net-like structure but combines the pre-trained ResNet34 as the encoder and adds dense residual units into the decoder. For this model, the final layer and cropping operation of the original U-Net model are removed to eliminate the model parameters. The mean and standard deviation of Precision, Recall, Intersection over Union (IoU), and F1 score are calculated under a five-fold training strategy to evaluate the model accuracy. The test experiments indicated that the proposed model performs well with the F1 of 92.6% and IoU of 86.24% in FRA extraction tasks. In particular, the ablation experiments and application experiments proved the effectiveness of the improvement strategy and the portability of the proposed D-ResUnet model, respectively. Compared with the other three state-of-the-art semantic segmentation models, the experiments demonstrate a clear accuracy advantage of the D-ResUnet model. For the FRA extraction task, this paper presents a promising approach that has refined extraction capability, high accuracy, and acceptable model complexity.
Keywords	floating raft aquaculture ; remote sensing ; deep learning ; residual unit ; synthetic aperture radar ; Science ; Q
Subject code	006
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Speed Estimation of Multiple Moving Objects from a Moving UAV Platform

Debojit Biswas / Hongbo Su / Chengyi Wang / Aleksandar Stevanovic

ISPRS International Journal of Geo-Information, Vol 8, Iss 6, p

2019 Volume 259

Abstract: Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval ... ...

Abstract	Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and robotics. In this study, we implemented a speed detection system for multiple moving objects on the ground from a moving platform in the air. A detect-and-track approach is used for primary tracking of the objects. Faster R-CNN (region-based convolutional neural network) is applied to detect the objects, and a discriminative correlation filter with CSRT (channel and spatial reliability tracking) is used for tracking. Feature-based image alignment (FBIA) is done for each frame to get the proper object location. In addition, SSIM (structural similarity index measurement) is performed to check how similar the current frame is with respect to the object detection frame. This measurement is necessary because the platform is moving, and new objects may be captured in a new frame. We achieved a speed accuracy of 96.80% with our framework with respect to the real speed of the objects.
Keywords	multiple object speed detection ; faster R-CNN ; discriminative correlation filter ; feature based image alignment ; structural similarity index measure ; Geography (General) ; G1-922
Subject code	004
Language	English
Publishing date	2019-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Developing a Method to Extract Building 3D Information from GF-7 Data

Jingyuan Wang / Xinli Hu / Qingyan Meng / Linlin Zhang / Chengyi Wang / Xiangchen Liu / Maofan Zhao

Remote Sensing, Vol 13, Iss 4532, p

2021 Volume 4532

Abstract: The three-dimensional (3D) information of buildings can describe the horizontal and vertical development of a city. The GaoFen-7 (GF-7) stereo-mapping satellite can provide multi-view and multi-spectral satellite images, which can clearly describe the ... ...

Abstract	The three-dimensional (3D) information of buildings can describe the horizontal and vertical development of a city. The GaoFen-7 (GF-7) stereo-mapping satellite can provide multi-view and multi-spectral satellite images, which can clearly describe the fine spatial details within urban areas, while the feasibility of extracting building 3D information from GF-7 image remains understudied. This article establishes an automated method for extracting building footprints and height information from GF-7 satellite imagery. First, we propose a multi-stage attention U-Net (MSAU-Net) architecture for building footprint extraction from multi-spectral images. Then, we generate the point cloud from the multi-view image and construct normalized digital surface model (nDSM) to represent the height of off-terrain objects. Finally, the building height is extracted from the nDSM and combined with the results of building footprints to obtain building 3D information. We select Beijing as the study area to test the proposed method, and in order to verify the building extraction ability of MSAU-Net, we choose GF-7 self-annotated building dataset and a public dataset (WuHan University (WHU) Building Dataset) for model testing, while the accuracy is evaluated in detail through comparison with other models. The results are summarized as follows: (1) In terms of building footprint extraction, our method can achieve intersection-over-union indicators of 89.31% and 80.27% for the WHU Dataset and GF-7 self-annotated datasets, respectively; these values are higher than the results of other models. (2) The root mean square between the extracted building height and the reference building height is 5.41 m, and the mean absolute error is 3.39 m. In summary, our method could be useful for accurate and automatic 3D building information extraction from GF-7 satellite images, and have good application potential.
Keywords	GF-7 image ; building footprint ; building height ; multi-view ; deep learning ; point cloud ; Science ; Q
Subject code	720
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Investigation of adsorption/desorption behavior of Cr(VI) at the presence of inorganic and organic substance in membrane capacitive deionization (MCDI)

Chen, Lin / Chengyi Wang / Shanshan Liu / Liang Zhu

Journal of environmental sciences (China). 2019 Apr., v. 78

2019

Abstract: The adsorption and desorption behavior of Cr(VI) in membrane capacitive deionization (MCDI) was investigated systematically in the presence of bovine serum albumin (BSA) and KCl with different concentrations, respectively. Results revealed that Cr(VI) ... ...

Abstract	The adsorption and desorption behavior of Cr(VI) in membrane capacitive deionization (MCDI) was investigated systematically in the presence of bovine serum albumin (BSA) and KCl with different concentrations, respectively. Results revealed that Cr(VI) absorption was enhanced and the adsorption amount for Cr(VI) increased from 155.7 to 190.8 mg/g when KCl concentration increased from 100 to 200 mg/L in the adsorption process, which was attributed to the stronger driving force. However, the adsorption amount sharply decreased to 90.2 mg/g when KCl concentration reached up to 1000 mg/L suggesting the negative effect for Cr(VI) removal that high KCl concentration had. As for the effect of BSA on ion adsorption, the amount for Cr (VI) significantly declined to 78.3 mg/g and pH was found to be an important factor contributing to this significant reduction. Then, the desorption performance was also conducted and it was obtained that the presence of KCl had negligible effect on Cr(VI) desorption, while promoted by the addition of BSA. The incomplete desorption was obtained and the residual chromium ions onto the electrode after desorption was detected via energy-dispersive X-ray spectroscopy (EDS). Based on above analysis, the enhanced removal mechanism for Cr(VI) in MCDI was found to be consisted of ion adsorption onto electrode surface, the redox reaction of Cr(VI) into Cr(III) and precipitation, which was demonstrated by X-ray photoelectron spectroscopy (XPS) and scanning electron microscope (SEM).
Keywords	X-ray photoelectron spectroscopy ; absorption ; adsorption ; bovine serum albumin ; chromium ; deionization ; desorption ; electrodes ; energy-dispersive X-ray analysis ; ions ; organic matter ; pH ; potassium chloride ; redox reactions ; scanning electron microscopes ; scanning electron microscopy
Language	English
Dates of publication	2019-04
Size	p. 303-314.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1092300-7
ISSN	1878-7320 ; 1001-0742
ISSN (online)	1878-7320
ISSN	1001-0742
DOI	10.1016/j.jes.2018.11.005
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Combining Segmentation Network and Nonsubsampled Contourlet Transform for Automatic Marine Raft Aquaculture Area Extraction from Sentinel-1 Images

Yi Zhang / Chengyi Wang / Yuan Ji / Jingbo Chen / Yupeng Deng / Jing Chen / Yongshi Jie

Remote Sensing, Vol 12, Iss 4182, p

2020 Volume 4182

Abstract: Marine raft aquaculture (MFA) plays an important role in the marine economy and ecosystem. With the characteristics of covering a large area and being sparsely distributed in sea area, MFA monitoring suffers from the low efficiency of field survey and ... ...

Abstract	Marine raft aquaculture (MFA) plays an important role in the marine economy and ecosystem. With the characteristics of covering a large area and being sparsely distributed in sea area, MFA monitoring suffers from the low efficiency of field survey and poor data of optical satellite imagery. Synthetic aperture radar (SAR) satellite imagery is currently considered to be an effective data source, while the state-of-the-art methods require manual parameter tuning under the guidance of professional experience. To preclude the limitation, this paper proposes a segmentation network combined with nonsubsampled contourlet transform (NSCT) to extract MFA areas using Sentinel-1 images. The proposed method is highlighted by several improvements based on the feature analysis of MFA. First, the NSCT was applied to enhance the contour and orientation features. Second, multiscale and asymmetric convolutions were introduced to fit the multisize and strip-like features more effectively. Third, both channel and spatial attention modules were adopted in the network architecture to overcome the problems of boundary fuzziness and area incompleteness. Experiments showed that the method can effectively extract marine raft culture areas. Although further research is needed to overcome the problem of interference caused by excessive waves, this paper provides a promising approach for periodical monitoring MFA in a large area with high efficiency and acceptable accuracy.
Keywords	marine raft aquaculture ; Sentinel-1 ; nonsubsampled contourlet transform ; semantic segmentation ; fully convolutional network ; Science ; Q
Subject code	333
Language	English
Publishing date	2020-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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