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Real-Time semantic segmentation via an Efficient Multi-Column Network

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Journal of Computer Science & Technology 2022年第6期37卷 1478-1491页

作者： Cheng-Li Peng Jia-Yi Ma Electronic Information School Wuhan UniversityWuhan 430072China

Existing semantic segmentation networks based on the multi-column structure can hardly satisfy the efficiency and precision requirements simultaneously due to their shallow spatial *** this paper,we propose a new efficient multi-column network termed as LadderNet to address this *** LadderNet includes two branches where the spatial branch generates high-resolution output feature map and the context branch encodes accurate semantic *** particular,we first propose a channel attention fusion block and a global context module to enhance the information encoding ability of the context ***,a new branch fusion method,i.e.,fusing some middle feature maps of the context branch into the spatial branch,is developed to improve the depth of the spatial ***,we design a feature fusing module to enhance the fusion quality of these two branches,leading to a more efficient *** compare our model with other state-of-the-arts on PASCAL VOC 2012 and Cityscapes *** results demonstrate that,compared with other state-of-the-art methods,our LadderNet can achieve average 1.25%mIoU improvement with comparable or less computation.

关键词： semantic segmentation real time multi-column network deep learning

A Lightweight Road Scene semantic segmentation Algorithm

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Computers, Materials & Continua 2023年第11期77卷 1929-1948页

作者： Jiansheng Peng Qing Yang Yaru Hou College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi547000China

In recent years,with the continuous deepening of smart city construction,there have been significant changes and improvements in the field of intelligent *** semantic segmentation of road scenes has important practical significance in the fields of automatic driving,transportation planning,and intelligent transportation ***,the current mainstream lightweight semantic segmentation models in road scene segmentation face problems such as poor segmentation performance of small targets and insufficient refinement of segmentation ***,this article proposes a lightweight semantic segmentation model based on the LiteSeg model improvement to address these *** model uses the lightweight backbone network MobileNet instead of the LiteSeg backbone network to reduce the network parameters and computation,and combines the Coordinate Attention(CA)mechanism to help the network capture long-distance *** the same time,by combining the dependencies of spatial information and channel information,the Spatial and Channel Network(SCNet)attention mechanism is proposed to improve the feature extraction ability of the ***,a multiscale transposed attention encoding(MTAE)module was proposed to obtain features of different resolutions and perform feature *** this paper,the proposed model is verified on the Cityscapes *** experimental results show that the addition of SCNet and MTAE modules increases the mean Intersection over Union(mIoU)of the original LiteSeg model by 4.69%.On this basis,the backbone network is replaced with MobileNet,and the CA model is added at the same *** the cost of increasing the minimum model parameters and computing costs,the mIoU of the original LiteSeg model is increased by 2.46%.This article also compares the proposed model with some current lightweight semantic segmentation models,and experiments show that the comprehensive performance of the proposed model is the best,especially in

关键词： semantic segmentation lightweight road scenes multi-scale transposition attention encoding(MTAE)

semantic segmentation of pyramidal neuron skeletons using geometric deep learning

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Journal of Innovative Optical Health Sciences 2023年第6期16卷 69-76页

作者： Lanlan Li Jing Qi Yi Geng Jingpeng Wu Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University FuzhouFujian 350116P.R.China Center for Computational Neuroscience Flatiron Institute New York 10010USA

Neurons can be abstractly represented as skeletons due to the filament nature of *** the rapid development of imaging and image analysis techniques,an increasing amount of neuron skeleton data is being *** some scienti fic studies,it is necessary to dissect the axons and dendrites,which is typically done manually and is both tedious and *** automate this process,we have developed a method that relies solely on neuronal skeletons using Geometric Deep Learning(GDL).We demonstrate the effectiveness of this method using pyramidal neurons in mammalian brains,and the results are promising for its application in neuroscience studies.

关键词： Pyramidal neuron geometric deep learning neuron skeleton semantic segmentation point cloud.

A labor-free index-guided semantic segmentation approach for urban vegetation mapping from high-resolution true color imagery

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International Journal of Digital Earth 2023年第1期16卷 1640-1660页

作者： Peng Zhang Cong Lin Shanchuan Guo Wei Zhang Hong Fang Peijun Du Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjingPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingPeople’s Republic of China c Nanjing Research Institute of Surveying Mapping&Geotechnical InvestigationCo.LtdNanjingPeople’s Republic of China

Accurate and timely information on urban vegetation(UV)can be used as an important indicator to estimate the health of *** to the low cost of RGB cameras,true color imagery(TCI)has been widely used for high spatial resolution UV ***,the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples,*** address this issue,an index-guided semantic segmentation(IGSS)framework is proposed in this ***,a novel cross-scale vegetation index(CSVI)is calculated by the combination of TCI and Sentinel-2 images,and the index value can be used to provide an initial UV ***,reliable UV and non-UV samples are automatically generated for training the semantic segmentation model,and then the refined UV map can be *** experimental results show that the proposed CSVI outperformed the existingfive RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds,and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72%∼88.16%and an F1 score of 87.73%∼88.37%,which is comparable with the fully-supervised method.

关键词： Urban vegetation mapping Sustainable Development Goals(SDGs) cross-scale vegetation index(CSVI) semantic segmentation high-resolution true color imagery(TCI)

Graph-Segmenter:graph transformer with boundary-aware attention for semantic segmentation

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Frontiers of Computer Science 2024年第5期18卷 97-108页

作者： Zizhang WU Yuanzhu GAN Tianhao XU Fan WANG Computer Vision Perception Department of ZongMu Technology Shanghai 201203China Faculty of Electrical Engineering Information TechnologyPhysicsTechnical University of BraunschweigBraunschweig 38106Germany

Thetransformer-based semantic segmentation approaches,which divide the image into different regions by sliding windows and model the relation inside each window,have achieved outstanding ***,since the relation modeling between windows was not the primary emphasis of previous work,it was not fully *** address this issue,we propose a Graph-Segmenter,including a graph transformer and a boundary-aware attention module,which is an effective network for simultaneously modeling the more profound relation between windows in a global view and various pixels inside each window as a local one,and for substantial low-cost boundary ***,we treat every window and pixel inside the window as nodes to construct graphs for both views and devise the graph *** introduced boundary-awareattentionmoduleoptimizes theedge information of the target objects by modeling the relationship between the pixel on the object's *** experiments on three widely used semantic segmentation datasets(Cityscapes,ADE-20k and PASCAL Context)demonstrate that our proposed network,a Graph Transformer with Boundary-aware Attention,can achieve state-of-the-art segmentation performance.

关键词： graph transformer graph relation network boundary-aware attention semantic segmentation

Navigation algorithm based on semantic segmentation in wheat fields using an RGB-D camera

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Information Processing in Agriculture 2023年第4期10卷 475-490页

作者： Yan Song Feiyang Xu Qi Yao Jialin Liu Shuai Yang School of Engineering Anhui Agricultural UniversityHefei 230036PR China Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery Hefei 230036PR China

Determining the navigation line is critical for the automatic navigation of agricultural robots in the *** this research,considering a wheat field as the typical scenario,a novel navigation line extraction algorithm based on semantic segmentation is *** data containing horizontal parallax,height,and grayscale information(HHG)is constructed by combining re-encoded depth data and red-green-blue(RGB)*** HHG,RGB,and depth data are used to achieve scene recognition and navigation line extraction for a wheat *** method includes two main ***,the semantic segmentation of the wheat,ground,and background are performed using a fully convolutional network(FCN).Second,the navigation line is fitted in the camera coordinate system on the basis of the semantic segmentation result and the principle of camera pinhole *** segmentation model is trained using 508 randomly selected images from a data set,and the model is tested on 199 *** labelled data are used as the reference benchmark,the mean intersection over union(mIoU)of the HHG data is greater than 95%,which is the highest among the three types of *** semantic segmentation methods based on the RGB and HHG data show higher navigation line extraction accuracy rates(with the absolute value of the angle deviation less than 5)than the compared *** mean and standard deviation of the angle deviation of the two methods are within 0.1and 2.0,while the mean and standard deviation of the distance deviation are less than 30 mm and 60 mm,*** values meet the basic requirements of agricultural machinery field *** novelty of this work is the proposal of a navigation line extraction algorithm based on semantic segmentation in wheat *** method is high in accuracy and robustness to interference from crop occlusion.

关键词： Fully convolutional network Navigation line extraction semantic segmentation Visual navigation

A Random Fusion of Mix 3D and Polar Mix to Improve semantic segmentation Performance in 3D Lidar Point Cloud

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 845-862页

作者： Bo Liu Li Feng Yufeng Chen School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Computer Science and Artificial Intelligence Chaohu UniversityChaohu238000China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive TechnologyShiyan442002China

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro

关键词： 3D lidar point cloud data augmentation RandomFusion semantic segmentation

A Lane Detection Method Based on semantic segmentation

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Computer Modeling in Engineering & Sciences 2020年第3期122卷 1039-1053页

作者： Ling Ding Huyin Zhang Jinsheng Xiao Cheng Shu Shejie Lu School of Computer Science Wuhan UniversityWuhan430072China. College of Computer Science and Technology Hubei University of Science and TechnologyXianning437100China. School of Electronic Information Wuhan UniversityWuhan430072China.

This paper proposes a novel method of lane detection,which adopts VGG16 as the basis of convolutional neural network to extract lane line features by cavity convolution,wherein the lane lines are divided into dotted lines and solid *** the field of experience through hollow convolution,the full connection layer of the network is discarded,the last largest pooling layer of the VGG16 network is removed,and the processing of the last three convolution layers is replaced by hole *** the same time,CNN adopts the encoder and decoder structure mode,and uses the index function of the maximum pooling layer in the decoder part to upsample the encoder in a counter-pooling manner,realizing semantic *** combined with the instance segmentation,and finally through the fitting to achieve the detection of the lane *** addition,the currently disclosed lane line data sets are relatively small,and there is no distinction between lane solid lines and dashed *** this end,our work made a lane line data set for the lane virtual and real identification,and based on the proposed algorithm effective verification of the data set achieved by the increased *** final test shows that the proposed method has a good balance between lane detection speed and accuracy,which has good robustness.

关键词： CNN VGG16 semantic segmentation instance segmentation lane detection

Image semantic segmentation for Autonomous Driving Based on Improved U-Net

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 787-801页

作者： Chuanlong Sun Hong Zhao Liang Mu Fuliang Xu Laiwei Lu College of Mechanical and Electrical Engineering Qingdao UniversityQingdao266071China

Image semantic segmentation has become an essential part of autonomous *** further improve the generalization ability and the robustness of semantic segmentation algorithms,a lightweight algorithm network based on Squeeze-and-Excitation Attention Mechanism(SE)and Depthwise Separable Convolution(DSC)is ***,Adam-GC,an Adam optimization algorithm based on Gradient Compression(GC),is proposed to improve the training speed,segmentation accuracy,generalization ability and stability of the algorithm *** verify and compare the effectiveness of the algorithm network proposed in this paper,the trained networkmodel is used for experimental verification and comparative test on the Cityscapes semantic segmentation *** validation and comparison results show that the overall segmentation results of the algorithmnetwork can achieve 78.02%MIoU on Cityscapes validation set,which is better than the basic algorithm network and the other latest semantic segmentation algorithms *** meeting the stability and accuracy requirements,it has a particular significance for the development of image semantic segmentation.

关键词： Deep learning semantic segmentation attention mechanism depthwise separable convolution gradient compression