A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network

Image semantic data have multilevel feature information. In the actual segmentation, the existing segmentation algorithms have some limitations, resulting in the fact that the final segmentation accuracy is too small. To solve this problem, a segmentation algorithm of image semantic sequence data based on graph convolution network is constructed. The graph convolution network is used to construct the image search process. The semantic sequence data are extracted. After the qualified data points are accumulated, the gradient amplitude forms complete rotation field and no scatter field in the diffusion process, which enhances the application scope of the algorithm, controls the accuracy of the segmentation algorithm, and completes the construction of the data segmentation algorithm. After the experimental dataset is prepared and the semantic segmentation direction is defined, we compare our method with four methods. The results show that the segmentation algorithm designed in this paper has the highest accuracy.

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YOLOMask, an Instance Segmentation Algorithm Based on Complementary Fusion Network

Mathematics ◽

10.3390/math9151766 ◽

2021 ◽

Vol 9 (15) ◽

pp. 1766

Author(s):

Jiang Hua ◽

Tonglin Hao ◽

Liangcai Zeng ◽

Gui Yu

Keyword(s):

Semantic Segmentation ◽

Segmentation Algorithm ◽

Data Sets ◽

Network Technology ◽

Category Information ◽

Time Performance ◽

Public Data ◽

Segmentation Algorithms ◽

Neural Network Technology ◽

Instance Segmentation

Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of neural network technology, semantic segmentation algorithms based on deep learning can obtain the category information of each pixel. However, the algorithm cannot effectively distinguish each object of the same category, so YOLOMask, an instance segmentation algorithm based on complementary fusion network, is proposed in this paper. Experimental results on public data sets COCO2017 show that the proposed fusion network can accurately obtain the category and location information of each instance and has good real-time performance.

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The Segmentation of Road Scenes Based on Improved ESPNet Model

Security and Communication Networks ◽

10.1155/2021/1681952 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Ran Jin ◽

Tongrui Yu ◽

Xiaozhen Han ◽

Yunpeng Liu

Keyword(s):

Image Segmentation ◽

Semantic Segmentation ◽

Automated Driving ◽

Computing Power ◽

Current Image ◽

Segmentation Accuracy ◽

Sample Data ◽

Segmentation Algorithms ◽

The Many ◽

Deep Learning Model

Image segmentation is an important research in image processing and machine vision in which automated driving can be seen the main application scene of image segmentation algorithms. Due to the many constraints of power supply and communication in in-vehicle systems, the vast majority of current image segmentation algorithms are implemented based on the deep learning model. Despite the ultrahigh segmentation accuracy, the problem of mesh artifacts and segmentation being too severe is obvious, and the high cost, computational, and power consumption devices required are difficult to apply in real-world scenarios. It is the focus of this paper to construct a road scene segmentation model with simple structure and no need of large computing power under the premise of certain accuracy. In this paper, the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) model is introduced in detail. On this basis, an improved ESPNet model is proposed based on ESPNet. Firstly, the network structure of the ESPNet model is optimized, and then, the model is optimized by using a small amount of weakly labeled and unlabeled scene sample data. Finally, the new model is applied to video image segmentation based on dash cam. It is verified on Cityscape, PASCAL VOC 2012, and other datasets that the algorithm proposed in this paper is faster, and the amount of parameters required is less than 1% of other algorithms, so it is suitable for mobile terminals.

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A GF-3 SAR Image Dataset of Road Segmentation

Information Technology And Control ◽

10.5755/j01.itc.50.1.27987 ◽

2021 ◽

Vol 50 (1) ◽

pp. 89-101

Author(s):

Zengguo Sun ◽

Mingmin Zhao ◽

Bai Jia

Keyword(s):

Semantic Segmentation ◽

Synthetic Aperture ◽

Experimental Result ◽

Sar Image ◽

Average Precision ◽

Sar Images ◽

Segmentation Accuracy ◽

Segmentation Algorithms ◽

Image Dataset ◽

Road Segmentation

We constructed a GF-3 SAR image dataset based on road segmentation to boost the development of GF-3 synthetic aperture radar (SAR) image road segmentation technology and make GF-3 SAR images be applied to practice better. We selected 23 scenes of GF-3 SAR images in Shaanxi, China, cut them into road chips with 512 × 512 pixels, and then labeled the dataset using LabelMe labeling tool. The dataset consists of 10026 road chips, and these road images are from different GF-3 imaging modes, so there is diversity in resolution and polarization. Three segmentation algorithms such as Multi-task Network Cascades (MNC), Fully Convolutional Instance-aware Semantic Segmentation (FCIS), and Mask Region Convolutional Neural Networks (Mask R-CNN) are trained by using the dataset. The experimental result measures including Average Precision (AP) and Intersection over Union (IoU) show that segmentation algorithms work well with this dataset, and the segmentation accuracy of Mask R-CNN is the best, which demonstrates the validity of the dataset we constructed.

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3D Semantic Segmentation Algorithm for Indoor Scenes based on Long-term Memory

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) ◽

10.1109/iciea48937.2020.9248387 ◽

2020 ◽

Author(s):

Ziyang Liu ◽

Weihai Chen ◽

Jianhua Wang ◽

Xingming Wu ◽

Haosong Yue ◽

...

Keyword(s):

Semantic Segmentation ◽

Segmentation Algorithm ◽

Long Term Memory ◽

Term Memory ◽

Indoor Scenes

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Real-time 2D–3D door detection and state classification on a low-power device

SN Applied Sciences ◽

10.1007/s42452-021-04588-3 ◽

2021 ◽

Vol 3 (5) ◽

Author(s):

João Gaspar Ramôa ◽

Vasco Lopes ◽

Luís A. Alexandre ◽

S. Mogo

Keyword(s):

Low Power ◽

Real Time ◽

Object Classification ◽

Semantic Segmentation ◽

Detection Algorithm ◽

Power Device ◽

Indoor Environments ◽

State Classification ◽

Segmentation Algorithms ◽

Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

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Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Remote Sensing ◽

10.3390/rs13101975 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1975

Author(s):

Lin Wang ◽

Yuzhen Zhou ◽

Qiao Hu ◽

Zhenghong Tang ◽

Yufeng Ge ◽

...

Keyword(s):

Early Detection ◽

Early Development ◽

Spatial Resolution ◽

Early Stage ◽

Semantic Segmentation ◽

Development Stage ◽

Unmanned Aircraft ◽

Economic Losses ◽

Juniperus Virginiana ◽

Segmentation Algorithms

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.

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A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms

Machine Vision and Applications ◽

10.1007/s00138-021-01196-4 ◽

2021 ◽

Vol 32 (3) ◽

Author(s):

Dimitrios Bellos ◽

Mark Basham ◽

Tony Pridmore ◽

Andrew P. French

Keyword(s):

Time Series ◽

Knowledge Transfer ◽

Semantic Segmentation ◽

Real World Data ◽

Transfer Scheme ◽

X Ray ◽

Segmentation Accuracy ◽

X Ray Computed ◽

Temporal Events ◽

Time Critical

AbstractOver recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available. When the X-ray exposure time has to be limited, undersampled tomograms (in terms of their component projections) are attained. This low number of projections offers low-quality reconstructions that are difficult to segment. Here, we consider CT time-series (i.e. 4D data), where the limited time for capturing fast-occurring temporal events results in the time-series tomograms being necessarily undersampled. Fortunately, in these collections, it is common practice to obtain representative highly sampled tomograms before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the time-series’ undersampled CTs, by training with the earlier highly sampled representative CTs. Our single network can offer two desired outputs while only training once, with the denoised output improving the accuracy of the final segmentation. Our method is able to outperform state-of-the-art methods in the task of semantic segmentation and offer comparable results in regard to denoising. Additionally, we propose a knowledge transfer scheme using synthetic tomograms. This not only allows accurate segmentation and denoising using less real-world data, but also increases segmentation accuracy. Finally, we make our datasets, as well as the code, publicly available.

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Fault Detection by Using Instance Segmentation

10.2523/iptc-21249-ms ◽

2021 ◽

Author(s):

Donglin Zhu ◽

Lei Li ◽

Rui Guo ◽

Shifan Zhan

Keyword(s):

Fault Detection ◽

Seismic Data ◽

Semantic Segmentation ◽

Data Interpretation ◽

Machine Learning Methods ◽

Segmentation Algorithms ◽

Direct Recognition ◽

First Time ◽

Seismic Images ◽

Instance Segmentation

Abstract Fault detection is an important, but time-consuming task in seismic data interpretation. Traditionally, seismic attributes, such as coherency (Marfurt et al., 1998) and curvature (Al-Dossary et al., 2006) are used to detect faults. Recently, machine learning methods, such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data (Wu et al., 2019). The most used algorithm is U-Net (Ronneberger et al., 2015), which can accurately and efficiently provide probability maps of faults. However, probabilities of faults generated by semantic segmentation algorithms are not sufficient for direct recognition of fault types and reconstruction of fault surfaces. To address this problem, we propose, for the first time, a workflow to use instance segmentation algorithm to detect different fault lines. Specifically, a modified CNN (LaneNet; Neven et al., 2018) is trained using automatically generated synthetic seismic images and corresponding labels. We then test the trained CNN using both synthetic and field collected seismic data. Results indicate that the proposed workflow is accurate and effective at detecting faults.

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