The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected...A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.展开更多
Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infr...Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed.This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels.Among them,LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library,while motion frame difference method can detect the moving regions of the image,improve the integrity of target regions such as camouflage,sheltering and deformation.In order to integrate the advantages of the two methods,the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced.The enhancement module of the network strengthened and screened the targets,and realized the background suppression of infrared images.Based on the experiments,the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated,and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets,and it’s detection performance was far better than the comparison algorithm.The experiment results indicated that,compared with traditional infrared target detection methods,the proposed method could detect the infrared invasion target more accurately,and suppress the background noise more effectively.展开更多
A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a ...A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.展开更多
In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swa...In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles(UAVs).First,the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers.Next,the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance.In order to validate the effectiveness of the proposed algorithm,experiments are conducted on four datasets.For the PASCAL VOC dataset,the proposed algorithm achieves the mean average precision(mAP)of 85.4 on the VOC 2007 test set.With regard to the detection in optical remote sensing(DIOR)dataset,the proposed algorithm achieves 73.9 mAP.For vehicle detection in aerial imagery(VEDAI)dataset,the detection accuracy of small land vehicle(slv)targets reaches 97.4 mAP.For unmanned aerial vehicle detection and tracking(UAVDT)dataset,the proposed BPMFPN Det achieves the mAP of 48.75.Compared with the previous state-of-the-art methods,the results obtained by the proposed algorithm are more competitive.The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.展开更多
Three-dimensional(3D)reconstruction based on aerial images has broad prospects,and feature matching is an important step of it.However,for high-resolution aerial images,there are usually problems such as long time,mis...Three-dimensional(3D)reconstruction based on aerial images has broad prospects,and feature matching is an important step of it.However,for high-resolution aerial images,there are usually problems such as long time,mismatching and sparse feature pairs using traditional algorithms.Therefore,an algorithm is proposed to realize fast,accurate and dense feature matching.The algorithm consists of four steps.Firstly,we achieve a balance between the feature matching time and the number of matching pairs by appropriately reducing the image resolution.Secondly,to realize further screening of the mismatches,a feature screening algorithm based on similarity judgment or local optimization is proposed.Thirdly,to make the algorithm more widely applicable,we combine the results of different algorithms to get dense results.Finally,all matching feature pairs in the low-resolution images are restored to the original images.Comparisons between the original algorithms and our algorithm show that the proposed algorithm can effectively reduce the matching time,screen out the mismatches,and improve the number of matches.展开更多
因为它的 insensitivity， 3D 脸识别吸引越来越多的注意到照明和姿势的变化。有在这个话题要解决的许多关键问题，例如 3D 脸表示和有效多特征熔化。在这份报纸，一个新奇 3D 脸识别算法被建议，它的性能在 BJUT-3D 脸数据库上被表明...因为它的 insensitivity， 3D 脸识别吸引越来越多的注意到照明和姿势的变化。有在这个话题要解决的许多关键问题，例如 3D 脸表示和有效多特征熔化。在这份报纸，一个新奇 3D 脸识别算法被建议，它的性能在 BJUT-3D 脸数据库上被表明。这个算法选择脸表面性质和相对关系矩阵的原则部件为脸表示特征。为每个特征的类似公制被定义。特征熔化策略被建议。它基于菲希尔是线性加权的策略线性判别式分析。最后，介绍算法在 BJUT-3D 脸数据库上被测试。算法和熔化策略的表演是令人满意的，这被结束。展开更多
【目的】煤矿井下普遍存在低照度、弱纹理和结构化的特征退化场景,导致视觉SLAM(visual simultaneous localization and mapping)系统面临有效特征不足或误匹配率高的问题,严重制约了其定位的准确性和鲁棒性。【方法】提出一种基于边缘...【目的】煤矿井下普遍存在低照度、弱纹理和结构化的特征退化场景,导致视觉SLAM(visual simultaneous localization and mapping)系统面临有效特征不足或误匹配率高的问题,严重制约了其定位的准确性和鲁棒性。【方法】提出一种基于边缘感知增强的视觉SLAM方法。首先,构建了边缘感知约束的低光图像增强模块。通过自适应尺度的梯度域引导滤波器优化Retinex算法,以获得纹理清晰光照均匀的图像,从而显著提升了在低光照和不均匀光照条件下特征提取性能。其次,在视觉里程计中构建了边缘感知增强的特征提取和匹配模块,通过点线特征融合策略有效增强了弱纹理和结构化场景中特征的可检测性和匹配准确性。具体使用边缘绘制线特征提取算法(edge drawing lines,EDLines)提取线特征,定向FAST和旋转BRIEF点特征提取算法(oriented fast and rotated brief,ORB)提取点特征,并利用基于网格运动统计(grid-based motion statistics,GMS)和比值测试匹配算法进行精确匹配。最后,将该方法与ORB-SLAM2、ORB-SLAM3在TUM数据集和煤矿井下实景数据集上进行了全面实验验证,涵盖图像增强、特征匹配和定位等多个环节。【结果和结论】结果表明:(1)在TUM数据集上的测试结果显示,所提方法与ORB-SLAM2相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了4%~38.46%、8.62%~50%;与ORB-SLAM3相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了0~61.68%、3.63%~47.05%。(2)在煤矿井下实景实验中,所提方法的定位轨迹更接近于相机运动参考轨迹。(3)有效提高了视觉SLAM在煤矿井下特征退化场景中的准确性和鲁棒性,为视觉SLAM技术在煤矿井下的应用提供了技术解决方案。研究面向井下特征退化场景的视觉SLAM方法,对于推动煤矿井下移动式装备机器人化具有重要意义。展开更多
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金supported partly by the National Basic Research Program of China (2005CB724303)the National Natural Science Foundation of China (60671062) Shanghai Leading Academic Discipline Project (B112).
文摘A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.
基金This work was supported by the National Natural Science Foundation of China(grant number:61671470)the National Key Research and Development Program of China(grant number:2016YFC0802904)the Postdoctoral Science Foundation Funded Project of China(grant number:2017M623423).
文摘Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed.This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels.Among them,LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library,while motion frame difference method can detect the moving regions of the image,improve the integrity of target regions such as camouflage,sheltering and deformation.In order to integrate the advantages of the two methods,the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced.The enhancement module of the network strengthened and screened the targets,and realized the background suppression of infrared images.Based on the experiments,the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated,and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets,and it’s detection performance was far better than the comparison algorithm.The experiment results indicated that,compared with traditional infrared target detection methods,the proposed method could detect the infrared invasion target more accurately,and suppress the background noise more effectively.
基金supported by the National Natural Science Foundation of China(61304097)the Projects of Major International(Regional)Joint Research Program NSFC(61120106010)the Foundation for Innovation Research Groups of the National National Natural Science Foundation of China(61321002)
文摘A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.
文摘In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles(UAVs).First,the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers.Next,the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance.In order to validate the effectiveness of the proposed algorithm,experiments are conducted on four datasets.For the PASCAL VOC dataset,the proposed algorithm achieves the mean average precision(mAP)of 85.4 on the VOC 2007 test set.With regard to the detection in optical remote sensing(DIOR)dataset,the proposed algorithm achieves 73.9 mAP.For vehicle detection in aerial imagery(VEDAI)dataset,the detection accuracy of small land vehicle(slv)targets reaches 97.4 mAP.For unmanned aerial vehicle detection and tracking(UAVDT)dataset,the proposed BPMFPN Det achieves the mAP of 48.75.Compared with the previous state-of-the-art methods,the results obtained by the proposed algorithm are more competitive.The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.
基金This work was supported by the Equipment Pre-Research Foundation of China(6140001020310).
文摘Three-dimensional(3D)reconstruction based on aerial images has broad prospects,and feature matching is an important step of it.However,for high-resolution aerial images,there are usually problems such as long time,mismatching and sparse feature pairs using traditional algorithms.Therefore,an algorithm is proposed to realize fast,accurate and dense feature matching.The algorithm consists of four steps.Firstly,we achieve a balance between the feature matching time and the number of matching pairs by appropriately reducing the image resolution.Secondly,to realize further screening of the mismatches,a feature screening algorithm based on similarity judgment or local optimization is proposed.Thirdly,to make the algorithm more widely applicable,we combine the results of different algorithms to get dense results.Finally,all matching feature pairs in the low-resolution images are restored to the original images.Comparisons between the original algorithms and our algorithm show that the proposed algorithm can effectively reduce the matching time,screen out the mismatches,and improve the number of matches.
基金Supported by National Natural Science Foundation of China （60533030） and Beijing Natural Science Foundation （4061001）
文摘因为它的 insensitivity， 3D 脸识别吸引越来越多的注意到照明和姿势的变化。有在这个话题要解决的许多关键问题，例如 3D 脸表示和有效多特征熔化。在这份报纸，一个新奇 3D 脸识别算法被建议，它的性能在 BJUT-3D 脸数据库上被表明。这个算法选择脸表面性质和相对关系矩阵的原则部件为脸表示特征。为每个特征的类似公制被定义。特征熔化策略被建议。它基于菲希尔是线性加权的策略线性判别式分析。最后，介绍算法在 BJUT-3D 脸数据库上被测试。算法和熔化策略的表演是令人满意的，这被结束。
文摘【目的】煤矿井下普遍存在低照度、弱纹理和结构化的特征退化场景,导致视觉SLAM(visual simultaneous localization and mapping)系统面临有效特征不足或误匹配率高的问题,严重制约了其定位的准确性和鲁棒性。【方法】提出一种基于边缘感知增强的视觉SLAM方法。首先,构建了边缘感知约束的低光图像增强模块。通过自适应尺度的梯度域引导滤波器优化Retinex算法,以获得纹理清晰光照均匀的图像,从而显著提升了在低光照和不均匀光照条件下特征提取性能。其次,在视觉里程计中构建了边缘感知增强的特征提取和匹配模块,通过点线特征融合策略有效增强了弱纹理和结构化场景中特征的可检测性和匹配准确性。具体使用边缘绘制线特征提取算法(edge drawing lines,EDLines)提取线特征,定向FAST和旋转BRIEF点特征提取算法(oriented fast and rotated brief,ORB)提取点特征,并利用基于网格运动统计(grid-based motion statistics,GMS)和比值测试匹配算法进行精确匹配。最后,将该方法与ORB-SLAM2、ORB-SLAM3在TUM数据集和煤矿井下实景数据集上进行了全面实验验证,涵盖图像增强、特征匹配和定位等多个环节。【结果和结论】结果表明:(1)在TUM数据集上的测试结果显示,所提方法与ORB-SLAM2相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了4%~38.46%、8.62%~50%;与ORB-SLAM3相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了0~61.68%、3.63%~47.05%。(2)在煤矿井下实景实验中,所提方法的定位轨迹更接近于相机运动参考轨迹。(3)有效提高了视觉SLAM在煤矿井下特征退化场景中的准确性和鲁棒性,为视觉SLAM技术在煤矿井下的应用提供了技术解决方案。研究面向井下特征退化场景的视觉SLAM方法,对于推动煤矿井下移动式装备机器人化具有重要意义。
