This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real...This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.展开更多
Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment.To precisely recognize these boundaries,a detection method for unmanned tractor...Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment.To precisely recognize these boundaries,a detection method for unmanned tractor plow areas based on RGB-Depth(RGB-D)cameras was proposed,and the feasibility of the detection method was analyzed.This method applied advanced computer vision technology to the field of agricultural automation.Adopting and improving the YOLOv5-seg object segmentation algorithm,first,the Convolutional Block Attention Module(CBAM)was integrated into Concentrated-Comprehensive Convolution Block(C3)to form C3CBAM,thereby enhancing the ability of the network to extract features from plow areas.The GhostConv module was also utilized to reduce parameter and computational complexity.Second,using the depth image information provided by the RGB-D camera combined with the results recognized by the YOLOv5-seg model,the mask image was processed to extract contour boundaries,align the contours with the depth map,and obtain the boundary distance information of the plowed area.Last,based on farmland information,the calculated average boundary distance was corrected,further improving the accuracy of the distance measurements.The experiment results showed that the YOLOv5-seg object segmentation algorithm achieved a recognition accuracy of 99%for plowed areas and that the ranging accuracy improved with decreasing detection distance.The ranging error at 5.5 m was approximately 0.056 m,and the average detection time per frame is 29 ms,which can meet the real-time operational requirements.The results of this study can provide precise guarantees for the autonomous operation of unmanned plowing units.展开更多
The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation.Segmentation is challenging with point cloud data due to...The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation.Segmentation is challenging with point cloud data due to substantial redundancy,fluctuating sample density and lack of apparent organization.The research area has a wide range of robotics applications,including intelligent vehicles,autonomous mapping and navigation.A number of researchers have introduced various methodologies and algorithms.Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I.methods.However,due to the specific problems of processing point clouds with deep neural networks,deep learning on point clouds is still in its initial stages.This study examines many strategies that have been presented to 3D instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3D segmentation.In these approaches’benefits,draw backs,and design mechanisms are studied and addressed.This study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets,as well as the most often used pipelines,their advantages and limits,insightful findings and intriguing future research directions.展开更多
In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. Remote sensing images usua...In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. Remote sensing images usually havelarge size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detectobjects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reducethe runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_PFC.The formal representation and details of two these procedures are presented in thispaper. The experiments on satellite image and remote sensing image datasets aregiven to evaluate proposed model. Validity indices and time consuming are usedto compare proposed model to picture fuzzy clustering model. The values ofvalidity indices show that picture fuzzy clustering integrated to MapReduce getsbetter quality of segmentation than using picture fuzzy clustering only. Moreover,on two selected image datasets, the run time of MPFC model is much less thanthat of picture fuzzy clustering.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant U20A20225,61833013in part by Shaanxi Provincial Key Research and Development Program under Grant 2022-GY111.
文摘This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.
基金financially supported by the National Key Research and Development Program(NKRDP)projects(Grant No.2023YFD2001100)Major Science and Technology Programs in Henan Province(Grant No.221100110800)Major Science and Technology Special Project of Henan Province(Longmen Laboratory First-class Project)(Grant No.231100220200).
文摘Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment.To precisely recognize these boundaries,a detection method for unmanned tractor plow areas based on RGB-Depth(RGB-D)cameras was proposed,and the feasibility of the detection method was analyzed.This method applied advanced computer vision technology to the field of agricultural automation.Adopting and improving the YOLOv5-seg object segmentation algorithm,first,the Convolutional Block Attention Module(CBAM)was integrated into Concentrated-Comprehensive Convolution Block(C3)to form C3CBAM,thereby enhancing the ability of the network to extract features from plow areas.The GhostConv module was also utilized to reduce parameter and computational complexity.Second,using the depth image information provided by the RGB-D camera combined with the results recognized by the YOLOv5-seg model,the mask image was processed to extract contour boundaries,align the contours with the depth map,and obtain the boundary distance information of the plowed area.Last,based on farmland information,the calculated average boundary distance was corrected,further improving the accuracy of the distance measurements.The experiment results showed that the YOLOv5-seg object segmentation algorithm achieved a recognition accuracy of 99%for plowed areas and that the ranging accuracy improved with decreasing detection distance.The ranging error at 5.5 m was approximately 0.056 m,and the average detection time per frame is 29 ms,which can meet the real-time operational requirements.The results of this study can provide precise guarantees for the autonomous operation of unmanned plowing units.
基金This research was supported by the BB21 plus funded by Busan Metropolitan City and Busan Institute for Talent and Lifelong Education(BIT)and a grant from Tongmyong University Innovated University Research Park(I-URP)funded by Busan Metropolitan City,Republic of Korea.
文摘The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation.Segmentation is challenging with point cloud data due to substantial redundancy,fluctuating sample density and lack of apparent organization.The research area has a wide range of robotics applications,including intelligent vehicles,autonomous mapping and navigation.A number of researchers have introduced various methodologies and algorithms.Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I.methods.However,due to the specific problems of processing point clouds with deep neural networks,deep learning on point clouds is still in its initial stages.This study examines many strategies that have been presented to 3D instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3D segmentation.In these approaches’benefits,draw backs,and design mechanisms are studied and addressed.This study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets,as well as the most often used pipelines,their advantages and limits,insightful findings and intriguing future research directions.
基金funded by Thuyloi University Foundation for Science and Technologyunder Grant Number TLU.STF.19-02.
文摘In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. Remote sensing images usually havelarge size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detectobjects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reducethe runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_PFC.The formal representation and details of two these procedures are presented in thispaper. The experiments on satellite image and remote sensing image datasets aregiven to evaluate proposed model. Validity indices and time consuming are usedto compare proposed model to picture fuzzy clustering model. The values ofvalidity indices show that picture fuzzy clustering integrated to MapReduce getsbetter quality of segmentation than using picture fuzzy clustering only. Moreover,on two selected image datasets, the run time of MPFC model is much less thanthat of picture fuzzy clustering.
