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semantic segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

Computer Modeling in Engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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MG-SLAM: RGB-D SLAM Based on semantic segmentation for Dynamic Environment in the Internet of Vehicles

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Computers, Materials & Continua 2025年第2期82卷 2353-2372页

作者： Fengju Zhang Kai Zhu School of Mechanical Engineering Jiangsu University of TechnologyChangzhou213001China School of Automobile and Traffic Engineering Jiangsu University of TechnologyChangzhou213001China

The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.

关键词： Visual SLAM dynamic scene semantic segmentation GPU acceleration key segmentation frame

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KD-SegNet: Efficient semantic segmentation Network with Knowledge Distillation Based on Monocular Camera

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Computers, Materials & Continua 2025年第2期82卷 2001-2026页

作者： Thai-Viet Dang Nhu-Nghia Bui Phan Xuan Tan School of Mechanical Engieering Hanoi University of Science and TechnologyHanoi10000Vietnam College of Engineering Shibaura Institute of TechnologyTokyo135-8548Japan

Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid pooling blocks are utilized to decode multi-scale feature maps, creating a complete semantic map inference. The student model not only preserves the strong segmentation performance from the teacher model but also improves the inference speed of the prediction results. The proposed method exhibits a clear advantage over conventional convolutional neural network (CNN) models, as evident from the conducted experiments. Furthermore, the proposed model also shows remarkable improvement in processing speed when compared with light-weight models such as MobileNetV2 and EfficientNet based on latency and throughput parameters. The proposed KD-SegNet model obtains an accuracy of 96.3% and a mIoU (mean Intersection over Union) of 77%, outperforming the performance of existing models by more than 15% on the same training dataset. The suggested method has an average training time that is only 0.51 times less than same field models, while still achieving comparable segmentation performance. Hence, the semantic segmentation frames are collected, forming the motion trajectory for the system in the environment. Overall, this architecture shows great promise for the development of knowledge-based systems for MR’s navigation.

关键词： Mobile robot navigation semantic segmentation knowledge distillation pyramid scene parsing fully convolutional networks

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semantic segmentation of agricultural images:A survey

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Information Processing in Agriculture 2024年第2期11卷 172-186页

作者： Zifei Luo Wenzhu Yang Yunfeng Yuan Ruru Gou Xiaonan Li School of Cyber Security and Computer Hebei UniversityHebei 071002China Hebei Machine Vision Engineering Research Center BaodingHebei 071002China College of Education Hebei UniversityHebei 071002China

As an important research topic in recent years,semantic segmentation has been widely applied to image understanding problems in various *** the successful application of deep learning methods in machine vision,the superior performance has been transferred to agricultural image processing by combining them with traditional *** segmentation methods have revolutionized the development of agricultural automation and are commonly used for crop cover and type analysis,pest and disease identification,*** frst give a review of the recent advances in traditional and deep learning methods for semantic segmentation of agricultural images according to different segmentation *** we introduce the traditional methods that can effectively utilize the original image information and the powerful performance of deep learningbased ***,we outline their applications in agricultural image *** our literature,we identify the challenges in agricultural image segmentation and summarize the innovative developments that address these *** robustness of the existing segmentation methods for processing complex images still needs to be improved urgently,and their generalization abilities are also *** particular,the limited number of labeled samples is a roadblock to new developed deep learning methods for their training and *** this,segmentation methods that augment the dataset or incorporate multimodal information enable deep learning methods to further improve the segmentation *** review provides a reference for the application of image semantic segmentation in the field of agricultural informatization.

关键词： semantic segmentation Agricultural images Deep learning Convolution neural networks

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semantic segmentation-based semantic communication system for image transmission

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Digital Communications and Networks 2024年第3期10卷 519-527页

作者： Jiale Wu Celimuge Wu Yangfei Lin Tsutomu Yoshinaga Lei Zhong Xianfu Chen Yusheng Ji The University of Electro-Communication Tokyo182-8585Japan Toyota Motor Corporation Tokyo112-8701Japan VTT Technical Research Centre of Finland OuluFinland National Institute of Informatics(NII) Tokyo101-8430Japan

With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between Regions Of Interest (ROI) and Regions Of Non-Interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.

关键词： semantic Communication semantic segmentation Image transmission Image compression Deep learning

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semantic segmentation by Using Down-Sampling and Subpixel Convolution: DSSC-UNet

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Computers, Materials & Continua 2023年第4期75卷 683-696页

作者： Young-Man Kwon Sunghoon Bae Dong-Keun Chung Myung-Jae Lim Department of Medical IT Eulji University553Sanseong-daeroSujeong-guSeongnam-siGyeonggi-do13135Korea

Recently, semantic segmentation has been widely applied toimage processing, scene understanding, and many others. Especially, indeep learning-based semantic segmentation, the U-Net with convolutionalencoder-decoder architecture is a representative model which is proposed forimage segmentation in the biomedical field. It used max pooling operationfor reducing the size of image and making noise robust. However, instead ofreducing the complexity of the model, max pooling has the disadvantageof omitting some information about the image in reducing it. So, thispaper used two diagonal elements of down-sampling operation instead ofit. We think that the down-sampling feature maps have more informationintrinsically than max pooling feature maps because of keeping the Nyquisttheorem and extracting the latent information from them. In addition,this paper used two other diagonal elements for the skip connection. Indecoding, we used Subpixel Convolution rather than transposed convolutionto efficiently decode the encoded feature maps. Including all the ideas, thispaper proposed the new encoder-decoder model called Down-Sampling andSubpixel Convolution U-Net (DSSC-UNet). To prove the better performanceof the proposed model, this paper measured the performance of the UNetand DSSC-UNet on the Cityscapes. As a result, DSSC-UNet achieved89.6% Mean Intersection OverUnion (Mean-IoU) andU-Net achieved 85.6%Mean-IoU, confirming that DSSC-UNet achieved better performance.

关键词： semantic segmentation encoder-decoder U-Net DSSC-UNet subpixel convolution down-sampling

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semantic segmentation for remote sensing images based on an AD-HRNet model

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International Journal of Digital Earth 2022年第1期15卷 2376-2399页

作者： Xue Yang Xiang Fan Mingjun Peng Qingfeng Guan Luliang Tang School of Geography and Information Engineering China University of GeosciencesWuhanPeople’s Republic of China Wuhan Geomatics Institute WuhanPeople’s Republic of China State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanPeople’s Republic of China

semantic segmentation for remote sensing images faces challenges of unbalanced category weight,rich context causing difficulties of recognition,blurred boundaries of multi-scale objects,and so *** address these problems,we propose a new model by combining HRNet with attention mechanisms and dilated convolution,denoted as:AD-HRNet for the semantic segmentation of remote sensing *** the framework of AD-HRNet,we obtained the weight value of each category based on an improved weighted cross-entropy function by introducing the median frequency balance method to solve the issue of class weight *** Shuffle-CBAM module with channel attention and spatial attention in AD-HRNet framework was applied to extract more global context information of images through slightly increasing the amount of *** address the problem of blurred boundaries caused by multi-scale object segmentation and edge segmentation,we developed an MDC-DUC module in AD-HRNet framework to capture the context information of multi-scale objects and the edge information of many irregular *** Postdam,Vaihingen,and SAMA-VTOL datasets as materials,we verified the performance of AD-HRNet by comparing with eight typical semantic segmentation *** results shown that AD-HRNet increases the mIoUs to 75.59%and 71.58%based on the Postdam and Vaihingen datasets,respectively.

关键词： semantic segmentation convolutional neural networks dilated convolution attention mechanism remote sensing

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semantic segmentation of track image based on deep neural network

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The Journal of China Universities of Posts and Telecommunications 2020年第5期27卷 23-33页

作者： Wang Zhaoying Zhou Junhua Liao Zhonghua Zhai Xiang Zhang Lianping School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Complex Product Advanced Manufacturing Kngineering Research Center Beijing Simulation CenterBeijing 100854China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China Alibaba Cloud Computing Beijing 100020China

In this paper,deep learning technology was utilited to solve the railway track recognition in intrusion detection *** railway track recognition can be viewed as semantic segmentation task which extends image processing to pixel level *** encoder-decoder architecture DeepLabv3+model was applied in this work due to its good performance in semantic segmentation *** images of the railway track collected from the video surveillance of the train cab were used as experiment dataset in this work,the following improvements were made to the *** first aspect deals with over-fitting problem due to the limited amount of training *** augmentation and transfer learning are applied consequently to rich the diversity of data and enhance model robustness during the training ***,different gradient descent methods are compared to obtain the optimal optimizer for training model *** third problem relates to data sample imbalance,cross entropy(CE)loss is replaced by focal loss(FL)to address the issue of serious imbalance between positive and negative *** of the improved DeepLabv3+model with above solutions is demonstrated by experiment results with different system parameters.

关键词： railway track recognition convolutional neural networks semantic segmentation DeepLabv3+

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semantic segmentation and YOLO Detector over Aerial Vehicle Images

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Computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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semantic segmentation of landcover for cropland mapping and area estimation using Machine Learning techniques

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Data Intelligence 2023年第2期5卷 370-387页

作者： Surabhi Lingwal Komal Kumar Bhatia Manjeet Singh Computer Science&Engineering G B Pant Institute of Engineering&TechnologyPauri Garhwal246194UttarakhandIndia Computer Engineering J C Bose Univerity of Science&TechnologyYMCAFaridabad121006HaryanaIndia

The paper has focussed on the global landcover for the identification of cropland *** growth and rapid industrialization are somehow disturbing the agricultural lands and eventually the food production needed for human *** agricultural land monitoring requires proper management of land *** paper has proposed a method for cropland mapping by semantic segmentation of landcover to identify the cropland boundaries and estimate the cropland areas using machine learning *** process has initially applied various filters to identify the features responsible for detecting the land boundaries through the edge detection *** images are masked or annotated to produce the ground truth for the label identification of croplands,rivers,buildings,and *** selected features are transferred to a machine learning model for the semantic segmentation *** methodology has applied Random Forest,which has compared to two other techniques,Support Vector Machine and Multilayer perceptron,for the semantic segmentation *** dataset is composed of satellite images collected from the QGIS *** paper has derived the conclusion that Random forest has given the best result for segmenting the image into different regions with 99%training accuracy and 90%test *** results are cross-validated by computing the Mean loU and kappa coefficient that shows 93%and 69%score value respectively for Random Forest,found maximum among *** paper has also calculated the area covered under the different segmented ***,Random Forest has produced promising results for semantic segmentation of landcover for cropland mapping.

关键词： Croplands semantic segmentation Machine Learning Random Forest Mask filters Edge detection Supportvectormachine

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