scholarly journals The Segmentation of Road Scenes Based on Improved ESPNet Model

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.

scholarly journals Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

2021 ◽  
Vol 13 (14) ◽  
pp. 2782
Author(s):  
Haoyu Wang ◽  
Zhanfeng Shen ◽  
Zihan Zhang ◽  
Zeyu Xu ◽  
Shuo Li ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Satellite Images ◽  
Evaluation Method ◽  
Remote Sensing Image ◽  
Region Merging ◽  
Segmentation Accuracy ◽  
Segmentation Algorithms ◽  
The Stability ◽  
Remote Sensing Image Processing ◽  
Previous Iteration

Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.

scholarly journals Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation

2010 ◽  
Vol 22 (2) ◽  
pp. 511-538 ◽  
Author(s):  
Srinivas C. Turaga ◽  
Joseph F. Murray ◽  
Viren Jain ◽  
Fabian Roth ◽  
Moritz Helmstaedter ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Ground Truth ◽  
Image Features ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Machine Learning Approach ◽  
Segmentation Accuracy ◽  
Segmentation Algorithms ◽  
Neuronal Processes ◽  
Segmentation Problem

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.

scholarly journals Adaptive Region Growing Image Segmentation Algorithms for Breast MRI

2019 ◽  
Vol 8 (3) ◽  
pp. 8729-8732
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Confidence Region ◽  
Region Growing ◽  
True Positive Rate ◽  
True Positive ◽  
Seed Selection ◽  
Segmentation Accuracy ◽  
Segmentation Algorithms ◽  
Positive Rate

Early detection and characterization of breast lesion are important for a better and effective treatment of breast cancer. In this paper, four different adaptive region growing image segmentation algorithms are compared. In fact, seed selection was a vital step in the success of region growing methods, so, better schemes for seed selection methods are proposed, namely, joint probabilistic seed selection (JPSS) and Generalised simulated annealing (GSA) based seed selection. The proposed region growing methods namely Fuzzy Region Growing (FRG) and Neutrosophic Region Growing (NRG) are integrated as JPSS-FRG and GSA-NRG frameworks. Another two methods are Scale Invariant Region growing (SiRG) and Fuzzy Neutrosophic Confidence Region growing (FNCRG). The results showed that FNCRG algorithm increases breast cancer detection rate on MRI breast images with the maximum of 93% is achieved. SiRG algorithm improves the true positive rate by 13% compared to existing methods. Further, GSA-NRG makes better segmentation accuracy by 9% and true positive rate by 12%. Also, JPSS-FRG algorithm enhances segmentation accuracy by 24% and improving the true positive rate by 27% compared to Region Growing-Cellular Neural Network (RG-CNN) and Seeded Region Growing-Particle swarm optimization (SRG-PSO) methods respectively

scholarly journals A GF-3 SAR Image Dataset of Road Segmentation

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.

scholarly journals Deep Learning based Brain Tumour Segmentation

2021 ◽  
Vol 19 ◽  
pp. 234-241
Author(s):  
Pattabiraman V. ◽  
Harshit Singh
Keyword(s):  
Artificial Intelligence ◽  
Image Segmentation ◽  
Deep Learning ◽  
Loss Function ◽  
Brain Mri ◽  
Semantic Segmentation ◽  
Tumour Segmentation ◽  
Segmentation Image ◽  
High Degree ◽  
Deep Learning Model

Artificial Intelligence has changed our outlook towards the whole world and it is regularly used to better understand all the data and information that surrounds us in our everyday lives. One such application of Artificial Intelligence in real world scenarios is extraction of data from various images and interpreting it in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application image segmentation has a variety of applications extending from complex medical field to regular pattern identification. The aim of this paper is to research about several FCNN based Semantic Segmentation techniques to develop a deep learning model that is able to segment tumours in brain MRI images to a high degree of precision and accuracy. The aim is to try several different architecture and experiment with several loss functions to improve the accuracy of our model and obtain the best model for our classification including newer loss function like dice loss function, hierarchical dice loss function cross entropy, etc.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zheshu Jia ◽  
Deyun Chen
Keyword(s):  
Sequence Data ◽  
Semantic Segmentation ◽  
Segmentation Algorithm ◽  
Semantic Data ◽  
Segmentation Accuracy ◽  
Segmentation Algorithms ◽  
Data Points ◽  
Scatter Field ◽  
Feature Information ◽  
Gradient Amplitude

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.

scholarly journals Semantic Image Segmentation in Duckietown

2021 ◽  
Vol 19 (3) ◽  
pp. 26-39
Author(s):  
D. E. Shabalina ◽  
K. S. Lanchukovskaya ◽  
T. V. Liakh ◽  
K. V. Chaika
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Image Segmentation ◽  
Semantic Segmentation ◽  
Network Algorithms ◽  
Segmentation Algorithms ◽  
Classical Semantic ◽  
Learning Frameworks ◽  
Segmentation Problem

The article is devoted to evaluation of the applicability of existing semantic segmentation algorithms for the “Duckietown” simulator. The article explores classical semantic segmentation algorithms as well as ones based on neural networks. We also examined machine learning frameworks, taking into account all the limitations of the “Duckietown” simulator. According to the research results, we selected neural network algorithms based on U-Net, SegNet, DeepLab-v3, FC-DenceNet and PSPNet networks to solve the segmentation problem in the “Duckietown” project. U-Net and SegNet have been tested on the “Duckietown” simulator.

scholarly journals Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 144
Author(s):  
Yuexing Han ◽  
Xiaolong Li ◽  
Bing Wang ◽  
Lu Wang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Images ◽  
Tumor Segmentation ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Segmentation Accuracy ◽  
Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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