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

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

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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

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Computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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DETReg Incorporating Semi-Supervised Learning for Object Detection in the Advanced Driver-Assistance Systems 1

DETReg Incorporating Semi-Supervised Learning for Object Det...

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1st International Conference on Robotics, engineering, Science, and technology, RESTCON 2024

作者： Nakano, Keita Matsushima, Kousuke National Institute of Technology Kurume College Advanced Engineering School Fukuoka Japan National Institute of Technology Kurume College Dept. of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350348842

In Advanced Driver-Assistance Systems (ADAS) and automatic driving, it is important to accurately recognize objects around the vehicle. DETReg is one of the unsupervised pre-Training methods using Transformer, which is self-supervised by combining localization and categorization. DETReg performs self-supervised learning on unlabeled images. Then, it extracted a wide range of features from rich aspects of the data and gained the flexibility to adapt to many variations. Fine tuning then used the labeled dataset of the target task to fine tune the model to fit the specific dataset. This allowed DETReg to achieve higher accuracy in the object detection task. However, it is difficult to learn DETReg efficiently because of its slow learning time. In this paper, we propose a new pre-Training method for object detection, called Semi-DETReg, that utilizes a few supervised labels during self-supervised learning. We incorporate semi-supervised learning into DETReg by using a portion of the supervised training data in the pre-Training to improve efficiency. We demonstrate the effectiveness of our method by conducting experiments and comparing our method to a similarly trained DETReg. © 2024 IEEE.

关键词： Object detection

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SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5724-5737页

作者： Yin, Yufeng Liu, Xiaoyan Zhang, Zichao Hunan University National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Innovation Institute of Industrial Design and Machine Intelligence Quanzhou-Hunan University Quanzhou362006 China

Multi-view stereo aims to recover the 3D model of a scene from a set of images. However, low-textured areas in the scene have always been a challenge in 3D reconstruction. In this work, we propose a segmentation-guided multi-scale anchor deformation patch multi-view stereo. Specifically, we use the Segment Anything Model to distinguish different instances in the scene, and propose an anchor deformation strategy guided by segmentation to adaptively generate a multi-scale anchor patch, so that the depth can be refined from coarse to fine scales. To propagate a better hypothesis for low-textured areas where photometric consistency is unreliable, we propose a non-local adaptive propagation scheme by using the segmented mask as a propagation domain. In order to reduce the interference of illumination on reconstruction completeness, we propose an outlier depth cost refinement guided by reliable points that improves the performance of depth estimation in unevenly illuminated areas. As a result, our method achieves state-of-the-art performance among traditional methods and exhibits better generalization capabilities on the ETH3D, Tanks and Temples, and DTU datasets. © 1991-2012 IEEE.

关键词： Image segmentation

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SDaDCS Remote Sensing Target Detection Algorithm

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Journal of Beijing institute of technology 2024年第6期33卷 556-569页

作者： Meijing Gao Yunjia Xie Xiangrui Fan Kunda Wang Sibo Chen Huanyu Sun Bingzhou Sun Xu Chen Ning Guan College of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China Beijing Aerospace Automatic Control Institute Beijing 100854China School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004HebeiChina Beijing Institute of Aerospace Intelligence and Information Beijing 100854China

In the field of remote sensing,the rapid and accurate acquisition of the category and location of airplanes has emerged as a prominent ***,remote sensing fuzzy imaging and complex environmental interference affect airplane ***,the inconsistency in the size of remote sensing images and the low accuracy of small target detection are crucial challenges that need to be *** tackle these issues,we propose a novel network SDaDCS(SAHI-data augmentation-dilation-channel and spatial attention)based on YOLOX model and the slicing aided hyper inference(SAHI)framework,a new data augmentation technique and dilation-channel and spatial(DCS)attention ***,we create a remote sensing dataset for airplane targets and introduce a new data augmentation technique based on the Rotate-Mixup and mixed data augmentation to enhance data *** DCS attention mechanism,which comprises the dilated convolution block,channel attention and spatial attention,is designed to bolster the feature extraction and discrimination of the *** address the challenges arised by the difficulties of detecting small targets,we integrate the YOLOX model with the SAHI *** results show that,when compared to the original YOLOX model,the proposed SDaDCS remote sensing target detection algorithm enhances overall accuracy by 13.6%.The experimental results validate the effectiveness of the proposed algorithm.

关键词： remote sensing target detection SDaDCS small target detection slicing aided hyper inference(SAHI) DCS attention mechanism

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Improved AKAZE Feature Matching Algorithm Based on Grid Motion Statistics 3

Improved AKAZE Feature Matching Algorithm Based on Grid Moti...

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3rd International Conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Wang, Hexin Ji, Xiu Lang, Chaoyue Yang, Jinglong School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Electrical and Information Engineering Changchun Institute of Technology Jilin Changchun130022 China

ISBN: (纸本)9781510670945

Aiming at the problem that AKAZE algorithm has slow feature extraction speed and low accuracy in the feature matching process, this paper proposes to improve AKAZE's feature matching algorithm based on grid statistical motion. Firstly, in the feature extraction stage, the proposed algorithm uses the oFAST algorithm instead of constructing scale space to extract feature points. Then, the M-LDB descriptor is used for feature point description. Finally, the BF algorithm is used to perform coarse matching of features, and the grid motion statistics (GMS) algorithm is added to achieve the purification of matching point pairs and complete the matching. The performance of the proposed algorithm was compared with the AKAZE and ORB algorithms in the experimental fields and grayscale graph groups. The results show that the improved algorithm not only improves the matching speed, which is more than 2 times faster than the AKAZE algorithm, but also maintains a high matching accuracy, which is similar to the AKAZE algorithm. © 2023 SPIE.

关键词： Feature extraction

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An Automatic Threshold Adjustment Algorithm for Thermal Fault Extraction 6

An Automatic Threshold Adjustment Algorithm for Thermal Faul...

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6th International Conference on Software engineering and Computer Science, CSECS 2023

作者： Diao, Weixuan Geng, Yingnan Jilin Institute of Chemical Technology Information and Control Engineering Jilin China Changchun Institute of Technology National Local Joint Engineering Research Center for IDNMCSOT Electrical and Information Engineering Jilin China

ISBN: (纸本)9798350306378

Infrared thermal (IT) image plays an important role in thermal fault detection for monitoring electric power equipment, because IT image can detect temperature difference information between normal and faulty power equipment. There are some problems with traditional thermal fault extraction methods, such as the low degree of automation and the inaccuracy of the extracted thermal fault area caused by the excessive detection range. To address these challenges, this paper proposes an automatic threshold adjustment algorithm for thermal fault extraction. Firstly, the IT image is converted to grayscale. Then, the statistical analysis of the grayscale histogram is performed. Based on the distribution of grayscale values, the quantity of grayscale differences defined in this paper is extracted at the boundary between the bright regions around the thermal fault area and the dark regions in the background. Subsequently, the peak of local minimal value is identified as the optimal threshold. Finally, the grayscale image is binarized using this optimal threshold, facilitating the extraction of thermal fault area. The algorithm was tested on more than 20 thermal fault images of power equipment, which shows that the algorithm can accurately extract the fault area. © 2023 IEEE.

关键词： Extraction

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Fuzzy-Model-Based Finite Frequency Fault Detection Filtering Design for Two-Dimensional Nonlinear Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2099-2110页

作者： Meng Wang Huaicheng Yan Jianbin Qiu Wenqiang Ji Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationSchool of Information Science and EngineeringEast China University of Science and TechnologyShanghai 200237China Research Institute of Intelligent Control and Systems School of AstronauticsHarbin Institute of TechnologyHarbin 150080China School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China IEEE

This article studies the fault detection filtering design problem for Roesser type two-dimensional(2-D)nonlinear systems described by uncertain 2-D Takagi-Sugeno(T-S)fuzzy ***,fuzzy Lyapunov functions are constructed and the 2-D Fourier transform is exploited,based on which a finite frequency fault detection filtering design method is proposed such that a residual signal is generated with robustness to external disturbances and sensitivity to *** has been shown that the utilization of available frequency spectrum information of faults and disturbances makes the proposed filtering design method more general and less conservative compared with a conventional nonfrequency based filtering design ***,with the proposed evaluation function and its threshold,a novel mixed finite frequency H_(∞)/H_(-)fault detection algorithm is developed,based on which the fault can be immediately detected once the evaluation function exceeds the ***,it is verified with simulation studies that the proposed method is effective and less conservative than conventional non-frequency and/or common Lyapunov function based filtering design methods.

关键词： Fault diagnosis finite frequency specifications mixed H_(∞)/H_(-)performance two-dimensional nonlinear systems

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Fine-Grained Recognition Framework For Ship Targets In SAR Images Based On Component Architecture 2

Fine-Grained Recognition Framework For Ship Targets In SAR I...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Pan, Yongchen Su, Can Zeng, Hongcheng Wang, Yamin Yang, Wei Li, Chunsheng Yu, Jiaqi Li, Dongliang Beihang University School of Electronic Information Engineering Beijing China Beijing Institute of Control and Electronics Technology Beijing China

ISBN: (纸本)9798331515669

Applying deep learning network models in SAR image processing has significantly improved speed of target recognition performance. Nevertheless, due to the SAR imaging principle distinct from optical images, these models struggle to distinguish strong scattering characteristics from clutter, resulting in a high false alarm rate in applications of target recognition. To ensure correctness practically, the expert analysis for discern various structures is strongly necessary for fine-grained recognition after possessing through integrating intelligent models, with extra time and manpower cost. This study introduces a fine-grained recognition framework for ship targets in SAR images based on component architecture. A ship targets dataset from Gaofen-3 SAR images, with three classified ship types is designed to train the model, including meticulous analysis and labeling for target categorization, axis orientation, and key component localization. Features of different dimensions extracted via pre-trained ResNet-50 to construct a weight dossier, and a multi-task joint detection mode are embedded into the framework to integrate the experts' ship recognition method built on component architecture into the intelligent network model. This yields an accuracy rate surpassing 89% for ship identification, eclipsing conventional intelligence models by a significant margin. The framework successfully bolsters the ability of target awareness affiliated with SAR ocean monitoring. © 2024 IEEE.

关键词： Radar imaging

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Distributed formation control of multiple aerial vehicles based on guidance route

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Chinese Journal of Aeronautics 2023年第3期36卷 368-381页

作者： Jinyong CHEN Rui ZHOU Guibin SUN Qingwei LI Ning ZHANG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Information Systems Engineering Laboratory Nanjing 210007China AVIC Xi’an Flight Automatic Control Research Institute Xi’an 710076China

Formation control of fixed-wing aerial vehicles is an important yet rarely addressed problem because of their complex dynamics and various motion constraints,such as nonholonomic and velocity *** guidance-route-based strategy has been demonstrated to be applicable to fixed-wing ***,it requires a global coordinator and there exists control lag,due to its own *** this reason,this paper presents a fully distributed guidance-route-based formation approach to address the aforementioned ***,a hop-count scheme is introduced to achieve distributed implementation,in which each aircraft chooses a neighbor with the minimum hop-count as a reference to generate its guidance route using only local ***,the model predictive control algorithm is employed to eliminate the control lag and achieve precise formation shape *** addition,the stall protection and collision avoidance are also ***,three numerical simulations demonstrate that our proposed approach can implement precise formation shape control of fixed-wing aircraft in a fully distributed manner.

关键词： Formation shape control Fixed-wing aircraft Guidance route Hop-count estimation Model predictive control

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