scholarly journals ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images

2021 ◽  
Vol 13 (4) ◽  
pp. 633
Author(s):  
Xiuwei Zhang ◽  
Yang Zhou ◽  
Jiaojiao Jin ◽  
Yafei Wang ◽  
Minhao Fan ◽  
...  
Keyword(s):  
Yellow River ◽  
Sampling Strategy ◽  
Semantic Segmentation ◽  
Practical Significance ◽  
River Ice ◽  
Semantic Features ◽  
Trend Prediction ◽  
Fine Grained ◽  
Actual Application ◽  
Uav Images

Accurate ice segmentation is one of the most crucial techniques for intelligent ice monitoring. Compared with ice segmentation, it can provide more information for ice situation analysis, change trend prediction, and so on. Therefore, the study of ice segmentation has important practical significance. In this study, we focused on fine-grained river ice segmentation using unmanned aerial vehicle (UAV) images. This has the following difficulties: (1) The scale of river ice varies greatly in different images and even in the same image; (2) the same kind of river ice differs greatly in color, shape, texture, size, and so on; and (3) the appearances of different kinds of river ice sometimes appear similar due to the complex formation and change procedure. Therefore, to perform this study, the NWPU_YRCC2 dataset was built, in which all UAV images were collected in the Ningxia–Inner Mongolia reach of the Yellow River. Then, a novel semantic segmentation method based on deep convolution neural network, named ICENETv2, is proposed. To achieve multiscale accurate prediction, we design a multilevel features fusion framework, in which multi-scale high-level semantic features and lower-level finer features are effectively fused. Additionally, a dual attention module is adopted to highlight distinguishable characteristics, and a learnable up-sampling strategy is further used to improve the segmentation accuracy of the details. Experiments show that ICENETv2 achieves the state-of-the-art on the NWPU_YRCC2 dataset. Finally, our ICENETv2 is also applied to solve a realistic problem, calculating drift ice cover density, which is one of the most important factors to predict the freeze-up data of the river. The results demonstrate that the performance of ICENETv2 meets the actual application demand.

scholarly journals ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

2020 ◽  
Vol 12 (2) ◽  
pp. 221 ◽  
Author(s):  
Xiuwei Zhang ◽  
Jiaojiao Jin ◽  
Zeze Lan ◽  
Chunjiang Li ◽  
Minhao Fan ◽  
...  
Keyword(s):  
State Of The Art ◽  
Yellow River ◽  
Semantic Segmentation ◽  
Ice Cover ◽  
River Ice ◽  
Superior Result ◽  
Aerial Vehicle ◽  
Deep Convolution Neural Network ◽  
Uav Image ◽  
Cover Density

River ice monitoring is of great significance for river management, ship navigation and ice hazard forecasting in cold-regions. Accurate ice segmentation is one most important pieces of technology in ice monitoring research. It can provide the prerequisite information for the calculation of ice cover density, drift ice speed, ice cover distribution, change detection and so on. Unmanned aerial vehicle (UAV) aerial photography has the advantages of higher spatial and temporal resolution. As UAV technology has become more popular and cheaper, it has been widely used in ice monitoring. So, we focused on river ice segmentation based on UAV remote sensing images. In this study, the NWPU_YRCC dataset was built for river ice segmentation, in which all images were captured by different UAVs in the region of the Yellow River, the most difficult river to manage in the world. To the best of our knowledge, this is the first public UAV image dataset for river ice segmentation. Meanwhile, a semantic segmentation deep convolution neural network by fusing positional and channel-wise attentive features is proposed for river ice semantic segmentation, named ICENET. Experiments demonstrated that the proposed ICENET outperforms the state-of-the-art methods, achieving a superior result on the NWPU_YRCC dataset.

A Fine-Grained River Ice Semantic Segmentation based on Attentive Features and Enhancing Feature Fusion

2021 ◽  
Author(s):  
rui wang ◽  
chengyu zheng ◽  
yanru jiang ◽  
Zhaoxin Wang ◽  
min ye ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Semantic Segmentation ◽  
River Ice ◽  
Fine Grained

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

scholarly journals Oxidation Roasting of Fine-Grained Carbonaceous Gold Ore: The Effect of Aeration Rate

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 558
Author(s):  
Hui Li ◽  
Wei Xiao ◽  
Jianping Jin ◽  
Yuexin Han
Keyword(s):  
Structure Analysis ◽  
Crystal Structure Analysis ◽  
Aeration Rate ◽  
Practical Significance ◽  
Gold Ore ◽  
Fine Grained ◽  
Roasting Process ◽  
Chemical Groups ◽  
Fine Carbon ◽  
Fine Gold

The oxidation roasting of carbon-bearing micro-fine gold can eliminate or weaken the robbing effect of carbonaceous materials and clay, and destroy the encapsulation of micro-fine gold. The micropores produced by gas escaping during the roasting process are conducive to the diffusion of leaching agents, thus enhancing the cyanide leaching of gold. In this paper, the influence of the aeration rate during roasting on the leaching rate of fine-grained carbonaceous gold ore and its mechanism were studied using thermodynamic calculations, crystal structure analysis, surface chemical groups and bonds analysis, microporous structure analysis, and surface morphology detection. Under suitable roasting conditions, the carbonaceous and pyrite in the ore are oxidized, while carbonate minerals such as dolomite and calcite as well as clay minerals are decomposed, and the gold-robbing materials lose their activity. The experimental results have theoretical and practical significance for the popularization and application of oxidation roasting technology of fine carbon-bearing gold ore.

River ice monitoring and change detection with multi-spectral and SAR images: application over yellow river

2021 ◽  
Author(s):  
Xiuwei Zhang ◽  
Yuanzeng Yue ◽  
Lin Han ◽  
Fei Li ◽  
Xiuzhong Yuan ◽  
...  
Keyword(s):  
Change Detection ◽  
Yellow River ◽  
River Ice ◽  
Sar Images

scholarly journals Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3065
Author(s):  
Libo Wang ◽  
Rui Li ◽  
Dongzhi Wang ◽  
Chenxi Duan ◽  
Teng Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Research Field ◽  
Learning Approaches ◽  
Fine Grained ◽  
Urban Scene ◽  
Fine Resolution ◽  
With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

TAD-Net: An approach for real-time action detection based on temporal convolution network and graph convolution network in digital twin shop-floor

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 10
Author(s):  
Qing Hong ◽  
Yifeng Sun ◽  
Tingyu Liu ◽  
Liang Fu ◽  
Yunfeng Xie
Keyword(s):  
Real Time ◽  
Sequence Data ◽  
Network Models ◽  
Human Action ◽  
Practical Significance ◽  
Shop Floor ◽  
Action Detection ◽  
Digital Twin ◽  
Actual Application ◽  
Production Processes

Background: Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly. Human action has a significant impact on the production safety and efficiency of a shop-floor, however, because of the high individual initiative of humans, it is difficult to realize real-time action detection in a digital twin shop-floor. Methods: We proposed a real-time detection approach for shop-floor production action. This approach used the sequence data of continuous human skeleton joints sequences as the input. We then reconstructed the Joint Classification-Regression Recurrent Neural Networks (JCR-RNN) based on Temporal Convolution Network (TCN) and Graph Convolution Network (GCN). We called this approach the Temporal Action Detection Net (TAD-Net), which realized real-time shop-floor production action detection. Results: The results of the verification experiment showed that our approach has achieved a high temporal positioning score, recognition speed, and accuracy when applied to the existing Online Action Detection (OAD) dataset and the Nanjing University of Science and Technology 3 Dimensions (NJUST3D) dataset. TAD-Net can meet the actual needs of the digital twin shop-floor. Conclusions: Our method has higher recognition accuracy, temporal positioning accuracy, and faster running speed than other mainstream network models, it can better meet actual application requirements, and has important research value and practical significance for standardizing shop-floor production processes, reducing production security risks, and contributing to the understanding of real-time production action.

scholarly journals A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3921 ◽  
Author(s):  
Wuttichai Boonpook ◽  
Yumin Tan ◽  
Yinghua Ye ◽  
Peerapong Torteeka ◽  
Kritanai Torsri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Building Detection ◽  
Network Training ◽  
Building Information ◽  
Open Standard ◽  
Uav Images ◽  
Uav Image

Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

scholarly journals Morphodynamic model of the lower Yellow River: flux or entrainment form for sediment mass conservation?

2018 ◽  
Vol 6 (4) ◽  
pp. 989-1010 ◽  
Author(s):  
Chenge An ◽  
Andrew J. Moodie ◽  
Hongbo Ma ◽  
Xudong Fu ◽  
Yuanfeng Zhang ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Yellow River ◽  
Mass Conservation ◽  
Transport Rate ◽  
Entrainment Rate ◽  
Lower Yellow River ◽  
The Lower Yellow River ◽  
Fine Grained ◽  
Sediment Transport Rate ◽  
Sediment Mass

Abstract. Sediment mass conservation is a key factor that constrains river morphodynamic processes. In most models of river morphodynamics, sediment mass conservation is described by the Exner equation, which may take various forms depending on the problem in question. One of the most widely used forms of the Exner equation is the flux-based formulation, in which the conservation of bed material is related to the stream-wise gradient of the sediment transport rate. An alternative form of the Exner equation, however, is the entrainment-based formulation, in which the conservation of bed material is related to the difference between the entrainment rate of bed sediment into suspension and the deposition rate of suspended sediment onto the bed. Here we represent the flux form in terms of the local capacity sediment transport rate and the entrainment form in terms of the local capacity entrainment rate. In the flux form, sediment transport is a function of local hydraulic conditions. However, the entrainment form does not require this constraint: only the rate of entrainment into suspension is in local equilibrium with hydraulic conditions, and the sediment transport rate itself may lag in space and time behind the changing flow conditions. In modeling the fine-grained lower Yellow River, it is usual to treat sediment conservation in terms of an entrainment (nonequilibrium) form rather than a flux (equilibrium) form, in consideration of the condition that fine-grained sediment may be entrained at one place but deposited only at some distant location downstream. However, the differences in prediction between the two formulations have not been comprehensively studied to date. Here we study this problem by comparing the results predicted by both the flux form and the entrainment form of the Exner equation under conditions simplified from the lower Yellow River (i.e., a significant reduction of sediment supply after the closure of the Xiaolangdi Dam). We use a one-dimensional morphodynamic model and sediment transport equations specifically adapted for the lower Yellow River. We find that in a treatment of a 200 km reach using a single characteristic bed sediment size, there is little difference between the two forms since the corresponding adaptation length is relatively small. However, a consideration of sediment mixtures shows that the two forms give very different patterns of grain sorting: clear kinematic waves occur in the flux form but are diffused out in the entrainment form. Both numerical simulation and mathematical analysis show that the morphodynamic processes predicted by the entrainment form are sensitive to sediment fall velocity. We suggest that the entrainment form of the Exner equation might be required when the sorting process of fine-grained sediment is studied, especially when considering relatively short timescales.

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