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Disparity estimation for multi-scale multi-sensor fusion
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作者 SUN Guoliang PEI Shanshan +2 位作者 LONG Qian ZHENG Sifa YANG Rui 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第2期259-274,共16页
The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results ... The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation. 展开更多
关键词 stereo vision light deterction and ranging(LiDAR) multi-sensor fusion multi-scale fusion disparity map
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MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View
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作者 Xiangyan Tang Chengchun Ruan +2 位作者 Xiulai Li Binbin Li Cebin Fu 《Computers, Materials & Continua》 SCIE EI 2024年第4期983-1003,共21页
Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variati... Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications. 展开更多
关键词 Small object detection YOLOv7 multi-scale attention spatial context
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A Lightweight Convolutional Neural Network with Hierarchical Multi-Scale Feature Fusion for Image Classification
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作者 Adama Dembele Ronald Waweru Mwangi Ananda Omutokoh Kube 《Journal of Computer and Communications》 2024年第2期173-200,共28页
Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware reso... Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline. 展开更多
关键词 MobileNet Image Classification Lightweight Convolutional Neural Network Depthwise Dilated Separable Convolution Hierarchical multi-scale Feature fusion
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Ship recognition based on HRRP via multi-scale sparse preserving method
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作者 YANG Xueling ZHANG Gong SONG Hu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第3期599-608,共10页
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba... In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance. 展开更多
关键词 ship target recognition high-resolution range profile(HRRP) multi-scale fusion kernel sparse preserving projection(MSFKSPP) feature extraction dimensionality reduction
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Clothing Parsing Based on Multi-Scale Fusion and Improved Self-Attention Mechanism
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作者 陈诺 王绍宇 +3 位作者 陆然 李文萱 覃志东 石秀金 《Journal of Donghua University(English Edition)》 CAS 2023年第6期661-666,共6页
Due to the lack of long-range association and spatial location information,fine details and accurate boundaries of complex clothing images cannot always be obtained by using the existing deep learning-based methods.Th... Due to the lack of long-range association and spatial location information,fine details and accurate boundaries of complex clothing images cannot always be obtained by using the existing deep learning-based methods.This paper presents a convolutional structure with multi-scale fusion to optimize the step of clothing feature extraction and a self-attention module to capture long-range association information.The structure enables the self-attention mechanism to directly participate in the process of information exchange through the down-scaling projection operation of the multi-scale framework.In addition,the improved self-attention module introduces the extraction of 2-dimensional relative position information to make up for its lack of ability to extract spatial position features from clothing images.The experimental results based on the colorful fashion parsing dataset(CFPD)show that the proposed network structure achieves 53.68%mean intersection over union(mIoU)and has better performance on the clothing parsing task. 展开更多
关键词 clothing parsing convolutional neural network multi-scale fusion self-attention mechanism vision Transformer
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Sub-Regional Infrared-Visible Image Fusion Using Multi-Scale Transformation 被引量:1
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作者 Yexin Liu Ben Xu +2 位作者 Mengmeng Zhang Wei Li Ran Tao 《Journal of Beijing Institute of Technology》 EI CAS 2022年第6期535-550,共16页
Infrared-visible image fusion plays an important role in multi-source data fusion,which has the advantage of integrating useful information from multi-source sensors.However,there are still challenges in target enhanc... Infrared-visible image fusion plays an important role in multi-source data fusion,which has the advantage of integrating useful information from multi-source sensors.However,there are still challenges in target enhancement and visual improvement.To deal with these problems,a sub-regional infrared-visible image fusion method(SRF)is proposed.First,morphology and threshold segmentation is applied to extract targets interested in infrared images.Second,the infrared back-ground is reconstructed based on extracted targets and the visible image.Finally,target and back-ground regions are fused using a multi-scale transform.Experimental results are obtained using public data for comparison and evaluation,which demonstrate that the proposed SRF has poten-tial benefits over other methods. 展开更多
关键词 image fusion infrared image visible image multi-scale transform
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An infrared and visible image fusion method based upon multi-scale and top-hat transforms 被引量:1
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作者 Gui-Qing He Qi-Qi Zhang +3 位作者 Hai-Xi Zhang Jia-Qi Ji Dan-Dan Dong Jun Wang 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第11期340-348,共9页
The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients ar... The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients around the zero value are very few, so we cannot sparsely represent low-frequency image information. The low-frequency component contains the main energy of the image and depicts the profile of the image. Direct fusion of the low-frequency component will not be conducive to obtain highly accurate fusion result. Therefore, this paper presents an infrared and visible image fusion method combining the multi-scale and top-hat transforms. On one hand, the new top-hat-transform can effectively extract the salient features of the low-frequency component. On the other hand, the multi-scale transform can extract highfrequency detailed information in multiple scales and from diverse directions. The combination of the two methods is conducive to the acquisition of more characteristics and more accurate fusion results. Among them, for the low-frequency component, a new type of top-hat transform is used to extract low-frequency features, and then different fusion rules are applied to fuse the low-frequency features and low-frequency background; for high-frequency components, the product of characteristics method is used to integrate the detailed information in high-frequency. Experimental results show that the proposed algorithm can obtain more detailed information and clearer infrared target fusion results than the traditional multiscale transform methods. Compared with the state-of-the-art fusion methods based on sparse representation, the proposed algorithm is simple and efficacious, and the time consumption is significantly reduced. 展开更多
关键词 infrared and visible image fusion multi-scale transform mathematical morphology top-hat trans- form
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Attention Guided Multi Scale Feature Fusion Network for Automatic Prostate Segmentation
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作者 Yuchun Li Mengxing Huang +1 位作者 Yu Zhang Zhiming Bai 《Computers, Materials & Continua》 SCIE EI 2024年第2期1649-1668,共20页
The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prosta... The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prostate segmentation,but due to the variability caused by prostate diseases,automatic segmentation of the prostate presents significant challenges.In this paper,we propose an attention-guided multi-scale feature fusion network(AGMSF-Net)to segment prostate MRI images.We propose an attention mechanism for extracting multi-scale features,and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder.In the decoder stage,a feature fusion module is proposed to obtain global context information.We evaluate our model on MRI images of the prostate acquired from a local hospital.The relative volume difference(RVD)and dice similarity coefficient(DSC)between the results of automatic prostate segmentation and ground truth were 1.21%and 93.68%,respectively.To quantitatively evaluate prostate volume on MRI,which is of significant clinical significance,we propose a unique AGMSF-Net.The essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation. 展开更多
关键词 Prostate segmentation multi-scale attention 3D Transformer feature fusion MRI
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The algorithm of 3D multi-scale volumetric curvature and its application 被引量:12
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作者 陈学华 杨威 +2 位作者 贺振华 钟文丽 文晓涛 《Applied Geophysics》 SCIE CSCD 2012年第1期65-72,116,共9页
To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature (MSVC) methodology is presented in this paper. W... To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature (MSVC) methodology is presented in this paper. We also propose a fast algorithm for computing 3D volumetric curvature. In comparison to conventional volumetric curvature attributes, its main improvements and key algorithms introduce multi-frequency components expansion in time-frequency domain and the corresponding multi-scale adaptive differential operator in the wavenumber domain, into the volumetric curvature calculation. This methodology can simultaneously depict seismic multi-scale features in both time and space. Additionally, we use data fusion of volumetric curvatures at various scales to take full advantage of the geologic features and anomalies extracted by curvature measurements at different scales. The 3D MSVC can highlight geologic anomalies and reduce noise at the same time. Thus, it improves the interpretation efficiency of curvature attributes analysis. The 3D MSVC is applied to both land and marine 3D seismic data. The results demonstrate that it can indicate the spatial distribution of reservoirs, detect faults and fracture zones, and identify their multi-scale properties. 展开更多
关键词 3D multi-scale volumetric curvature adaptive differential operator in wavenumber domain multi-frequency expansion in time-frequency domain fault detection fracture zone data fusion
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Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems 被引量:1
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作者 Hao Liu Fengwei Liang +2 位作者 Tianyu Hu Jichao Hong Huimin Ma 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2024年第2期405-414,共10页
Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale featu... Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical BESSs.Pearson correlation analysis is first employed to identify SOC-related parameters.These parameters are then input into a multi-layer GRU for point-wise feature extraction.Concurrently,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time intervals.Ultimately,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are rendered.Following extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction. 展开更多
关键词 Electric vehicle battery energy storage system(BESS) state-of-charge(SOC)prediction gated recurrent unit(GRU) multi-scale fusion(MSF).
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Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual
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作者 Wenjie Geng Zhiqiang Cao +3 位作者 Peiyu Guan Fengshui Jing Min Tan Junzhi Yu 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第1期244-256,共13页
Grasp detection plays a critical role for robot manipulation.Mainstream pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and efficiency.However,they usuall... Grasp detection plays a critical role for robot manipulation.Mainstream pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and efficiency.However,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are neglected.It is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains unsolved.Meanwhile,the channel information in high-level feature is also not well mined.Inevitably,the performance of grasp detection is degraded.To solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle residual.Both low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature fusion.Such a hierarchical fusion guarantees the quality of grasp prediction.Furthermore,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective branches.By such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the network.Besides,an information enhancement module is added before the encoder to reinforce input information.The proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method. 展开更多
关键词 grasp detection hierarchical multi-scale feature fusion skip connections with attention inverted shuffle residual
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Multi-Scale Feature Fusion Model for Bridge Appearance Defect Detection
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作者 Rong Pang Yan Yang +3 位作者 Aiguo Huang Yan Liu Peng Zhang Guangwu Tang 《Big Data Mining and Analytics》 EI CSCD 2024年第1期1-11,共11页
Although the Faster Region-based Convolutional Neural Network(Faster R-CNN)model has obvious advantages in defect recognition,it still cannot overcome challenging problems,such as time-consuming,small targets,irregula... Although the Faster Region-based Convolutional Neural Network(Faster R-CNN)model has obvious advantages in defect recognition,it still cannot overcome challenging problems,such as time-consuming,small targets,irregular shapes,and strong noise interference in bridge defect detection.To deal with these issues,this paper proposes a novel Multi-scale Feature Fusion(MFF)model for bridge appearance disease detection.First,the Faster R-CNN model adopts Region Of Interest(ROl)pooling,which omits the edge information of the target area,resulting in some missed detections and inaccuracies in both detecting and localizing bridge defects.Therefore,this paper proposes an MFF based on regional feature Aggregation(MFF-A),which reduces the missed detection rate of bridge defect detection and improves the positioning accuracy of the target area.Second,the Faster R-CNN model is insensitive to small targets,irregular shapes,and strong noises in bridge defect detection,which results in a long training time and low recognition accuracy.Accordingly,a novel Lightweight MFF(namely MFF-L)model for bridge appearance defect detection using a lightweight network EfficientNetV2 and a feature pyramid network is proposed,which fuses multi-scale features to shorten the training speed and improve recognition accuracy.Finally,the effectiveness of the proposed method is evaluated on the bridge disease dataset and public computational fluid dynamic dataset. 展开更多
关键词 defect detection multi-scale Feature fusion(MFF) Region Of Interest(ROl)alignment lightweight network
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Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions
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作者 Chih-Ta Yen Tz-Yun Chen +1 位作者 Un-Hung Chen Guo-Chang WangZong-Xian Chen 《Computers, Materials & Continua》 SCIE EI 2023年第1期83-99,共17页
A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study.The wearable device consisted of a six-axis sensor,Raspberry Pi 3,and a power bank.M... A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study.The wearable device consisted of a six-axis sensor,Raspberry Pi 3,and a power bank.Multiple kernel sizes were used in convolutional neural network(CNN)to evaluate their performance for extracting features.Moreover,a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner.The CNN achieved recognition of the four table tennis strokes.Experimental data were obtained from20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment.The data were collected to verify the performance of the proposed models for wearable devices.Finally,the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58%and 99.16%,respectively,for the four strokes.The accuracy for five-fold cross validation was 99.87%.This result also shows that the multi-scale convolutional neural network has better robustness after fivefold cross validation. 展开更多
关键词 Wearable devices deep learning six-axis sensor feature fusion multi-scale convolutional neural networks action recognit
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基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别方法
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作者 胡宏宇 黎烨宸 +3 位作者 张争光 曲优 何磊 高镇海 《汽车工程》 EI CSCD 北大核心 2024年第1期1-8,28,共9页
识别非驾驶行为是提高驾驶安全性的重要手段之一。目前基于骨架序列和图像的融合识别方法具有计算量大和特征融合困难的问题。针对上述问题,本文提出一种基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别模型(skeleton-image base... 识别非驾驶行为是提高驾驶安全性的重要手段之一。目前基于骨架序列和图像的融合识别方法具有计算量大和特征融合困难的问题。针对上述问题,本文提出一种基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别模型(skeleton-image based behavior recognition network,SIBBR-Net)。SIBBR-Net通过基于多尺度图的图卷积网络和基于局部视觉及注意力机制的卷积神经网络,充分提取运动和外观特征,较好地平衡了模型表征能力和计算量间的关系。基于手部运动的特征双向引导学习策略、自适应特征融合模块和静态特征空间上的辅助损失,使运动和外观特征间互相引导更新并实现自适应融合。最终在Drive&Act数据集进行算法测试,SIBBR-Net在动态标签和静态标签条件下的平均正确率分别为61.78%和80.42%,每秒浮点运算次数为25.92G,较最优方法降低了76.96%。 展开更多
关键词 驾驶员行为识别 多尺度骨架图 局部视觉上下文 多模态数据自适应融合
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面向城市复杂场景的多尺度监督融合变化检测
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作者 潘建平 谢鹏 +2 位作者 郭志豪 林娜 张慧娟 《遥感信息》 CSCD 北大核心 2024年第4期23-32,共10页
城市复杂场景中,地物形状多样,光照和成像角度变化大会导致变化检测结果受到干扰。为解决这些问题,文章提出了一种双上下文多尺度监督融合的网络模型(dual context multi-scale supervised fusion network model,DCMSFNet)。首先,在编... 城市复杂场景中,地物形状多样,光照和成像角度变化大会导致变化检测结果受到干扰。为解决这些问题,文章提出了一种双上下文多尺度监督融合的网络模型(dual context multi-scale supervised fusion network model,DCMSFNet)。首先,在编码部分使用双上下文增强模块获得地物丰富的全局上下文信息。在解码部分,采用级联的方法组合特征,然后通过自适应注意力模块捕捉不同尺度的变化关系,设计多尺度监督融合模块,增强深度网络融合,获得具有更高辨别能力的变化区域特征,将不同层级的输出结果与主网络的重构变化图融合形成最终的变化检测结果。该模型在LEVIR-CD和SYSU-CD变化检测数据集取得了较好的结果,F1-score分别提高了1.58%和2.17%,可更加精确识别复杂场景的变化区域,进一步减少无关因素引起的误检和漏检,且对目标地物边缘的检测更加平滑。 展开更多
关键词 深度学习 变化检测 双上下文增强 自适应注意力模块 多尺度监督融合
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基于上下文融合和注意力的安全帽检测方法
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作者 徐志刚 李宇根 朱红蕾 《计算机仿真》 2024年第8期204-209,共6页
安全帽检测是近年来目标检测在工业生产作业领域的一个研究热点。针对安全帽检测过程中容易出现的小尺度目标错检、漏检等问题,提出一种基于上下文融合和注意力的安全帽检测方法。方法通过利用混合域注意力强调目标关键特征信息,加强特... 安全帽检测是近年来目标检测在工业生产作业领域的一个研究热点。针对安全帽检测过程中容易出现的小尺度目标错检、漏检等问题,提出一种基于上下文融合和注意力的安全帽检测方法。方法通过利用混合域注意力强调目标关键特征信息,加强特征提取;同时,构建基于非局部注意模块的上下文信息融合结构,将底层全局上下文信息引入深层特征中,进一步细化深层语义信息;此外,利用感受野模块捕获多尺度特征和增大感受野,以减少小尺度目标在特征融合过程中出现特征信息丢失,以及预测过程中对小尺度目标不敏感的问题。实验分析表明,上述方法在安全帽佩戴数据集上对于安全帽检测的AP值达到93.10%,较原YOLOv4提升2.12%,mAP达到93.07%,较原YOLOv4提升1.39%。 展开更多
关键词 安全帽检测 上下文融合 注意力机制
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MFFNet:多级特征融合图像语义分割网络
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作者 王燕 南佩奇 《计算机科学与探索》 CSCD 北大核心 2024年第3期707-717,共11页
在图像语义分割任务中,大多数方法未将不同尺度、不同层次的特征充分利用就直接进行上采样,会造成一些有效信息被当成冗余信息而被摒弃,从而降低对某些细小类别和相似类别分割的准确性和敏感性。为此,提出一个多级特征融合网络(MFFNet)... 在图像语义分割任务中,大多数方法未将不同尺度、不同层次的特征充分利用就直接进行上采样,会造成一些有效信息被当成冗余信息而被摒弃,从而降低对某些细小类别和相似类别分割的准确性和敏感性。为此,提出一个多级特征融合网络(MFFNet)。MFFNet采用编码器-解码器结构,在编码阶段,通过上下文信息提取路径和空间信息提取路径分别获取上下文信息与空间细节信息,增强像素间关联性与边界准确性;解码阶段设计一条多级特征融合路径,利用混合双边融合模块融合上下文信息;利用高低特征融合模块融合深层信息与空间信息;利用全局通道融合模块获取不同通道之间的联系,实现不同尺度信息的全局融合。MFFNet网络在PASCAL VOC 2012和Cityscapes验证集上的平均交互比(MIoU)分别为80.70%和76.33%,取得了较好的分割结果。 展开更多
关键词 编码器-解码器 上下文信息 空间信息 特征融合
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融合知识和约束图的远程监督关系抽取方法
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作者 刘琼昕 牛文涛 王佳升 《北京理工大学学报》 EI CAS CSCD 北大核心 2024年第7期731-739,共9页
远程监督关系抽取通过自动标注数据减少人工标注成本,但存在句子标签噪声和关系长尾分布两个问题.为解决上述问题,提出一种融合知识图中实体信息以及实体和关系间约束的关系抽取方法.该方法对目标实体和其邻居实体的属性进行编码,对目... 远程监督关系抽取通过自动标注数据减少人工标注成本,但存在句子标签噪声和关系长尾分布两个问题.为解决上述问题,提出一种融合知识图中实体信息以及实体和关系间约束的关系抽取方法.该方法对目标实体和其邻居实体的属性进行编码,对目标实体和邻居实体构成的邻居图进行编码,对实体类型和关系间约束进行编码,并通过多源融合注意力模块进行信息整合,构建关系抽取模型.该方法在NYT-10数据集上的AUC值为0.524,P@100值为94.8%,长尾指标Hits@K较之前最先进模型均有提升,取得了优异表现,表明该方法融合实体信息和约束信息解决远程监督关系抽取两个主要问题的有效性. 展开更多
关键词 远程监督关系抽取 知识上下文 约束图 多源融合注意力
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面向无人驾驶场景的实时语义分割算法研究
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作者 文凯 韦胜男 杨一鹏 《计算机仿真》 2024年第1期144-148,414,共6页
针对现有无人驾驶场景中分割精度与运行速度之间难以取得平衡的问题,提出一种新型的实时语义分割算法。首先,利用残差结构(Residual)构建出高效特征提取单元(EFEU)以更好地感知空间和语义信息;其次,采用了一种双边结构,其中空间分支结... 针对现有无人驾驶场景中分割精度与运行速度之间难以取得平衡的问题,提出一种新型的实时语义分割算法。首先,利用残差结构(Residual)构建出高效特征提取单元(EFEU)以更好地感知空间和语义信息;其次,采用了一种双边结构,其中空间分支结合池化操作保留浅层空间信息,上下文分支用来提供大的感受野,捕获深层上下文信息,两条分支在网络的不同阶段合并,以加强不同层次之间的信息传播。最后结合深度可分离卷积和通道重排操作构建上下文融合模块,将不同层次之间的信息进行融合,进一步提高模型的分割效果。在常用的数据集上进行实验,实验结果证明了所提方法的有效性。 展开更多
关键词 实时语义分割 高效特征提取 双边结构 上下文融合
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基于双阶段特征解耦网络的单幅图像去雨方法
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作者 汤红忠 熊珮全 +2 位作者 王蔚 王晒雅 陈磊 《计算机辅助设计与图形学学报》 EI CSCD 北大核心 2024年第2期273-282,共10页
针对现有的单幅图像去雨方法无法有效地平衡背景图像细节恢复与有雨分量去除问题,提出一种基于双阶段特征解耦网络的单幅图像去雨方法,采用渐进式的学习方式从粗到细分阶段进行单幅图像去雨.首先构建压缩激励残差模块,实现背景图像与有... 针对现有的单幅图像去雨方法无法有效地平衡背景图像细节恢复与有雨分量去除问题,提出一种基于双阶段特征解耦网络的单幅图像去雨方法,采用渐进式的学习方式从粗到细分阶段进行单幅图像去雨.首先构建压缩激励残差模块,实现背景图像与有雨分量的初步分离;然后设计全局特征融合模块,其中特别引入特征解耦模块分离有雨分量和背景图像的特征,实现细粒度的图像去雨;最后利用重构损失、结构相似损失、边缘感知损失和纹理一致性损失构成的复合损失函数训练网络,实现高质量的无雨图像重构.实验结果表明,在Test100合成雨图数据集上,所提方法峰值信噪比为25.57dB,结构相似性为0.89;在100幅真实雨图上,所提方法的自然图像质量评估器为3.53,无参考图像空间质量评估器为20.16;在去雨后的RefineNet目标分割任务中,平均交并比为29.41%,平均像素精度为70.06%;视觉效果上,该方法能保留更多的背景图像特征,有效地辅助下游的目标分割任务的开展. 展开更多
关键词 特征解耦网络 压缩激励残差模块 全局特征融合模块 复合损失函数 单幅图像去雨
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