scholarly journals Semantic Segmentation of Underwater Images Based on Improved Deeplab

2020 ◽  
Vol 8 (3) ◽  
pp. 188
Author(s):  
Fangfang Liu ◽  
Ming Fang
Keyword(s):  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Correction Method ◽  
Target Object ◽  
Original Method ◽  
Indoor Navigation ◽  
Fine Tuning ◽  
Object Boundary ◽  
Current State ◽  
Segmentation Accuracy

Image semantic segmentation technology has been increasingly applied in many fields, for example, autonomous driving, indoor navigation, virtual reality and augmented reality. However, underwater scenes, where there is a huge amount of marine biological resources and irreplaceable biological gene banks that need to be researched and exploited, are limited. In this paper, image semantic segmentation technology is exploited to study underwater scenes. We extend the current state-of-the-art semantic segmentation network DeepLabv3 + and employ it as the basic framework. First, the unsupervised color correction method (UCM) module is introduced to the encoder structure of the framework to improve the quality of the image. Moreover, two up-sampling layers are added to the decoder structure to retain more target features and object boundary information. The model is trained by fine-tuning and optimizing relevant parameters. Experimental results indicate that the image obtained by our method demonstrates better performance in improving the appearance of the segmented target object and avoiding its pixels from mingling with other class’s pixels, enhancing the segmentation accuracy of the target boundaries and retaining more feature information. Compared with the original method, our method improves the segmentation accuracy by 3%.

scholarly journals Deep Neural Network-Based Semantic Segmentation of Microvascular Decompression Images

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1167
Author(s):  
Ruifeng Bai ◽  
Shan Jiang ◽  
Haijiang Sun ◽  
Yifan Yang ◽  
Guiju Li
Keyword(s):  
Semantic Segmentation ◽  
Fine Tuning ◽  
Cerebral Vessel ◽  
Current State ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Spatial Pyramid Pooling ◽  
Boundary Information ◽  
True Color ◽  
Good Research

Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial nerve segmentation based on true-color medical images is in urgent need and has good research and development prospects. We have extended the current state-of-the-art semantic-segmentation network DeepLabv3+ and used it as the basic framework. First, the feature distillation block (FDB) was introduced into the encoder structure to refine the extracted features. In addition, the atrous spatial pyramid pooling (ASPP) module was added to the decoder structure to enhance the retention of feature and boundary information. The proposed model was trained by fine tuning and optimizing the relevant parameters. Experimental results show that the encoder structure has better performance in feature refinement processing, improving target boundary segmentation precision, and retaining more feature information. Our method has a segmentation accuracy of 75.73%, which is 3% better than DeepLabv3+.

scholarly journals SEMANTIC SEGMENTATION OF POINT CLOUDS WITH POINTNET AND KPCONV ARCHITECTURES APPLIED TO RAILWAY TUNNELS

2020 ◽  
Vol V-2-2020 ◽  
pp. 281-288
Author(s):  
M. Soilán ◽  
A. Nóvoa ◽  
A. Sánchez-Rodríguez ◽  
B. Riveiro ◽  
P. Arias
Keyword(s):  
Three Dimensional ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Research Area ◽  
Transport Infrastructure ◽  
Infrastructure Monitoring ◽  
Current State ◽  
Model Training ◽  
Multi Class Classification

Abstract. Transport infrastructure monitoring has lately attracted increasing attention due to the rise in extreme natural hazards posed by climate change. Mobile Mapping Systems gather information regarding the state of the assets, which allows for more efficient decision-making. These systems provide information in the form of three-dimensional point clouds. Point cloud analysis through deep learning has emerged as a focal research area due to its wide application in areas such as autonomous driving. This paper aims to apply the pioneering PointNet, and the current state-of-the-art KPConv architectures to perform scene segmentation of railway tunnels, in order to validate their employability over heuristic classification methods. The approach is to perform a multi-class classification that classifies the most relevant components of tunnels: ground, lining, wiring and rails. Both architectures are trained from scratch with heuristically classified point clouds of two different railway tunnels. Results show that, while both architectures are suitable for the proposed classification task, KPConv outperforms PointNet with F1-scores over 97% for ground, lining and wiring classes, and over 90% for rails. In addition, KPConv is tested using transfer learning, which gives F1-scores slightly lower than for the model training from scratch but shows better generalization capabilities.

ADAPTIVE SEGMENTATION OF MEDICAL MR IMAGES BASED ON BIAS CORRECTION

2011 ◽  
Vol 11 (04) ◽  
pp. 813-826
Author(s):  
ZENGLIANG FU ◽  
YONGLIN SU ◽  
MING YE ◽  
YANPING LIN ◽  
CHENGTAO WANG
Keyword(s):  
Bias Correction ◽  
Bias Field ◽  
Correction Method ◽  
Initial Contour ◽  
Adaptive Segmentation ◽  
Mr Images ◽  
Object Boundary ◽  
Correct Position ◽  
Two Phase ◽  
Segmentation Accuracy

A two-phase model is introduced to extract clinically useful information from medical MR images. In the preprocessing phase, a refined bias correction method is adopted to reduce the influence of intensity inhomogeneity by removing the bias field, which paves the way for improving the subsequent segmentation accuracy. During image segmentation process, a novel adaptive level set technique is designed to capture the boundary of desired region. By virtue of adaptive driving term, the external force automatically changes its propagating direction when evolving curve goes through object boundary, which effectively prevents the final results deviating from correct position. Moreover, insensitivity to initial contour also enables its automatic applications. Experiments on synthetic and real MR images demonstrate the feasibility and robustness of the proposed method.

scholarly journals AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation

2020 ◽  
Author(s):  
Gege Zhang ◽  
Qinghua Ma ◽  
Licheng Jiao ◽  
Fang Liu ◽  
Qigong Sun
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
3D Point Cloud ◽  
Adversarial Learning ◽  
Adversarial Networks ◽  
Segmentation Accuracy ◽  
Segmentation Task

3D point cloud semantic segmentation has attracted wide attention with its extensive applications in autonomous driving, AR/VR, and robot sensing fields. However, in existing methods, each point in the segmentation results is predicted independently from each other. This property causes the non-contiguity of label sets in three-dimensional space and produces many noisy label points, which hinders the improvement of segmentation accuracy. To address this problem, we first extend adversarial learning to this task and propose a novel framework Attention Adversarial Networks (AttAN). With high-order correlations in label sets learned from the adversarial learning, segmentation network can predict labels closer to the real ones and correct noisy results. Moreover, we design an additive attention block for the segmentation network, which is used to automatically focus on regions critical to the segmentation task by learning the correlation between multi-scale features. Adversarial learning, which explores the underlying relationship between labels in high-dimensional space, opens up a new way in 3D point cloud semantic segmentation. Experimental results on ScanNet and S3DIS datasets show that this framework effectively improves the segmentation quality and outperforms other state-of-the-art methods.

scholarly journals PPANet: Point-Wise Pyramid Attention Network for Semantic Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mohammed A. M. Elhassan ◽  
YuXuan Chen ◽  
Yunyi Chen ◽  
Chenxi Huang ◽  
Jane Yang ◽  
...  
Keyword(s):  
Contextual Information ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Middle Part ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Attention Network ◽  
Feature Representations ◽  
Segmentation Accuracy ◽  
Road Segmentation

In recent years, convolutional neural networks (CNNs) have been at the centre of the advances and progress of advanced driver assistance systems and autonomous driving. This paper presents a point-wise pyramid attention network, namely, PPANet, which employs an encoder-decoder approach for semantic segmentation. Specifically, the encoder adopts a novel squeeze nonbottleneck module as a base module to extract feature representations, where squeeze and expansion are utilized to obtain high segmentation accuracy. An upsampling module is designed to work as a decoder; its purpose is to recover the lost pixel-wise representations from the encoding part. The middle part consists of two parts point-wise pyramid attention (PPA) module and an attention-like module connected in parallel. The PPA module is proposed to utilize contextual information effectively. Furthermore, we developed a combined loss function from dice loss and binary cross-entropy to improve accuracy and get faster training convergence in KITTI road segmentation. The paper conducted the training and testing experiments on KITTI road segmentation and Camvid datasets, and the evaluation results show that the proposed method proved its effectiveness in road semantic segmentation.

scholarly journals Deep Learning Methods Applied to 3D Point Clouds Based Instance Segmentation: A Review

2021 ◽  
Author(s):  
Desire Mulindwa Burume ◽  
Shengzhi Du
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Indoor Navigation ◽  
Future Directions ◽  
3D Point Clouds ◽  
Speed Accuracy ◽  
Learning Architectures ◽  
Instance Segmentation

Beyond semantic segmentation,3D instance segmentation(a process to delineate objects of interest and also classifying the objects into a set of categories) is gaining more and more interest among researchers since numerous computer vision applications need accurate segmentation processes(autonomous driving, indoor navigation, and even virtual or augmented reality systems…) This paper gives an overview and a technical comparison of the existing deep learning architectures in handling unstructured Euclidean data for the rapidly developing 3D instance segmentation. First, the authors divide the 3D point clouds based instance segmentation techniques into two major categories which are proposal based methods and proposal free methods. Then, they also introduce and compare the most used datasets with regard to 3D instance segmentation. Furthermore, they compare and analyze these techniques performance (speed, accuracy, response to noise…). Finally, this paper provides a review of the possible future directions of deep learning for 3D sensor-based information and provides insight into the most promising areas for prospective research.

scholarly journals A Novel Upsampling and Context Convolution for Image Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2170
Author(s):  
Khwaja Monib Sediqi ◽  
Hyo Jong Lee
Keyword(s):  
Spatial Information ◽  
Robot Vision ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Local Context ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Object Boundary ◽  
Benchmark Datasets ◽  
Convolution Method

Semantic segmentation, which refers to pixel-wise classification of an image, is a fundamental topic in computer vision owing to its growing importance in the robot vision and autonomous driving sectors. It provides rich information about objects in the scene such as object boundary, category, and location. Recent methods for semantic segmentation often employ an encoder-decoder structure using deep convolutional neural networks. The encoder part extracts features of the image using several filters and pooling operations, whereas the decoder part gradually recovers the low-resolution feature maps of the encoder into a full input resolution feature map for pixel-wise prediction. However, the encoder-decoder variants for semantic segmentation suffer from severe spatial information loss, caused by pooling operations or stepwise convolutions, and does not consider the context in the scene. In this paper, we propose a novel dense upsampling convolution method based on a guided filter to effectively preserve the spatial information of the image in the network. We further propose a novel local context convolution method that not only covers larger-scale objects in the scene but covers them densely for precise object boundary delineation. Theoretical analyses and experimental results on several benchmark datasets verify the effectiveness of our method. Qualitatively, our approach delineates object boundaries at a level of accuracy that is beyond the current excellent methods. Quantitatively, we report a new record of 82.86% and 81.62% of pixel accuracy on ADE20K and Pascal-Context benchmark datasets, respectively. In comparison with the state-of-the-art methods, the proposed method offers promising improvements.

scholarly journals Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 437
Author(s):  
Yuya Onozuka ◽  
Ryosuke Matsumi ◽  
Motoki Shino
Keyword(s):  
Visual Information ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Weighting Method ◽  
Personal Mobility ◽  
Human Understanding ◽  
Autonomous Mobility ◽  
Weakly Supervised ◽  
Traffic Rules

Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.

scholarly journals The Skeletal Muscle Emerges as a New Disease Target in Amyotrophic Lateral Sclerosis

2021 ◽  
Vol 11 (7) ◽  
pp. 671
Author(s):  
Oihane Pikatza-Menoio ◽  
Amaia Elicegui ◽  
Xabier Bengoetxea ◽  
Neia Naldaiz-Gastesi ◽  
Adolfo López de Munain ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Amyotrophic Lateral Sclerosis ◽  
Muscle Tissue ◽  
Motor Neurons ◽  
Neurodegenerative Disorder ◽  
Disease Onset ◽  
Fine Tuning ◽  
Current State ◽  
The Central Nervous System ◽  
Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder that leads to progressive degeneration of motor neurons (MNs) and severe muscle atrophy without effective treatment. Most research on ALS has been focused on the study of MNs and supporting cells of the central nervous system. Strikingly, the recent observations of pathological changes in muscle occurring before disease onset and independent from MN degeneration have bolstered the interest for the study of muscle tissue as a potential target for delivery of therapies for ALS. Skeletal muscle has just been described as a tissue with an important secretory function that is toxic to MNs in the context of ALS. Moreover, a fine-tuning balance between biosynthetic and atrophic pathways is necessary to induce myogenesis for muscle tissue repair. Compromising this response due to primary metabolic abnormalities in the muscle could trigger defective muscle regeneration and neuromuscular junction restoration, with deleterious consequences for MNs and thereby hastening the development of ALS. However, it remains puzzling how backward signaling from the muscle could impinge on MN death. This review provides a comprehensive analysis on the current state-of-the-art of the role of the skeletal muscle in ALS, highlighting its contribution to the neurodegeneration in ALS through backward-signaling processes as a newly uncovered mechanism for a peripheral etiopathogenesis of the disease.

scholarly journals Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2280
Author(s):  
Ching-Chang Wong ◽  
Li-Yu Yeh ◽  
Chih-Cheng Liu ◽  
Chi-Yi Tsai ◽  
Hisasuki Aoyama
Keyword(s):  
Neural Network ◽  
Robot Manipulator ◽  
Detection System ◽  
Semantic Segmentation ◽  
Target Object ◽  
Planning System ◽  
Normal Vector ◽  
Manipulation Planning ◽  
Keypoint Detection ◽  
3D Keypoint Detection

In this paper, a manipulation planning method for object re-orientation based on semantic segmentation keypoint detection is proposed for robot manipulator which is able to detect and re-orientate the randomly placed objects to a specified position and pose. There are two main parts: (1) 3D keypoint detection system; and (2) manipulation planning system for object re-orientation. In the 3D keypoint detection system, an RGB-D camera is used to obtain the information of the environment and can generate 3D keypoints of the target object as inputs to represent its corresponding position and pose. This process simplifies the 3D model representation so that the manipulation planning for object re-orientation can be executed in a category-level manner by adding various training data of the object in the training phase. In addition, 3D suction points in both the object’s current and expected poses are also generated as the inputs of the next operation stage. During the next stage, Mask Region-Convolutional Neural Network (Mask R-CNN) algorithm is used for preliminary object detection and object image. The highest confidence index image is selected as the input of the semantic segmentation system in order to classify each pixel in the picture for the corresponding pack unit of the object. In addition, after using a convolutional neural network for semantic segmentation, the Conditional Random Fields (CRFs) method is used to perform several iterations to obtain a more accurate result of object recognition. When the target object is segmented into the pack units of image process, the center position of each pack unit can be obtained. Then, a normal vector of each pack unit’s center points is generated by the depth image information and pose of the object, which can be obtained by connecting the center points of each pack unit. In the manipulation planning system for object re-orientation, the pose of the object and the normal vector of each pack unit are first converted into the working coordinate system of the robot manipulator. Then, according to the current and expected pose of the object, the spherical linear interpolation (Slerp) algorithm is used to generate a series of movements in the workspace for object re-orientation on the robot manipulator. In addition, the pose of the object is adjusted on the z-axis of the object’s geodetic coordinate system based on the image features on the surface of the object, so that the pose of the placed object can approach the desired pose. Finally, a robot manipulator and a vacuum suction cup made by the laboratory are used to verify that the proposed system can indeed complete the planned task of object re-orientation.

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