Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is prop...Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art color- based saliency detection methods.展开更多
A novel cast shadow detection approach was proposed.A stereo vision system was used to capture images instead of traditional single camera.It was based on an assumption that cast shadows were on a special plane.The im...A novel cast shadow detection approach was proposed.A stereo vision system was used to capture images instead of traditional single camera.It was based on an assumption that cast shadows were on a special plane.The image obtained from one camera was inversely projected to the plane and then transformed to the view from another camera.The points on the plane shared the same position between original image and the transformed image.As a result,the cast shadows can be detected.In order to improve the efficiency of cast shadow detection and decrease computational complexity,the obvious object areas in CIELAB color space were removed and the potential shadow areas were obtained.Experimental results demonstrate that the proposed approach can detect cast shadows accurately even under various illuminations.展开更多
An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points (GCPs) is presented. To decrease time complexity without losing matching precision, u...An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points (GCPs) is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.展开更多
The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Si...The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.展开更多
Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically ...Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar(InSAR),fail to accurately capture understory terrain due to limited penetration capabilities,resulting in elevation overestimation in densely vegetated areas.While airborne light detection and ranging(LiDAR)can provide high-accuracy DTMs,its limited spatial coverage and high acquisition cost hinder large-scale applications.Thus,there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models(DSMs).In this study,we propose a simple,interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation(GEDI)and Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)to construct a tree height surface model,which is then subtracted from the stereo-derived DSM to generate the final DTM.By directly incorporating LiDAR constraints,the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs,ensuring ease of implementation and broad applicability.In contrast to machine learning-based terrain modeling methods,which are often prone to overfitting and data bias,the proposed approach is simple,interpretable,and robust across diverse forested landscapes.The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model(DEM)and the forest and buildings removed DEM(FABDEM),a global bare-earth elevation model corrected for vegetation bias.The results indicate that the proposed DTM consistently outperforms the Copernicus DEM(CopDEM)and achieves accuracy comparable to FABDEM.In addition,its finer spatial resolution of 1 m,compared to the 30 m resolution of FABDEM,allows for more detailed terrain representation and better capture of fine-scale variation.This advantage is most pronounced in gently to moderately sloped areas,where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM.The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints,offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable.To illustrate the potential utility of the proposed DTM,we applied it to a fire risk mapping application based on topographic parameters such as slope,aspect,and elevation.This case highlights how improved terrain representation can support geospatial hazard assessments.展开更多
Electrical connectors are core functional components in aerospace electrical systems.Pin retraction may lead to signal transmission interruption and even system failure,directly affecting the reliability of electrical...Electrical connectors are core functional components in aerospace electrical systems.Pin retraction may lead to signal transmission interruption and even system failure,directly affecting the reliability of electrical equipment and causing incalculable consequences.We propose a high-precision pin-retraction detection method that integrates binocular stereo vision with a multi-constrained optimization matching algorithm,aiming to achieve universal recognition of pins across different connector models and robust detection of pin retraction in complex scenarios.In this study,the Delaunay triangulation algorithm is employed to eliminate the misidentified pins from the template matching algorithm.Furthermore,the pin recognition rate is enhanced to nearly 99.75%,and the accuracy of pin center positioning is significantly improved by integrating a contour fitting and positioning algorithm for pin points.Subsequently,the binocular matching of pins is achieved by combining probabilistic epipolar constraints with geometric constraints,thereby completing the threedimensional reconstruction of pin points.The Euclidean distance from the three-dimensional pin points to the reference plane is calculated as the pin retraction amount,enabling the quantitative measurement of pin retraction amount.Through the design of multiple experiments for measuring the pin retraction of different-type electrical connectors and the analysis of the results using the Kullback-Leibler(K-L)divergence,it is demonstrated that the system’s measurement accuracy is superior to 0.05 mm,with an repeatability error of less than 0.035 mm.The effectiveness of the proposed pin-retraction detection method is thus verified,and the detection efficiency over manual operations is greatly enhanced to meet the actual industrial inspection requirements.展开更多
为了提升汽车辅助系统对前方障碍物的检测效果并进一步获取精确的距离信息,文章提出了一种基于改进YOLOv8s的交通场景障碍物检测与双目测距方法。该方法以YOLOv8s(You Only Look Once v8s)网络为基础,首先在Backbone中引入EMA注意力机制...为了提升汽车辅助系统对前方障碍物的检测效果并进一步获取精确的距离信息,文章提出了一种基于改进YOLOv8s的交通场景障碍物检测与双目测距方法。该方法以YOLOv8s(You Only Look Once v8s)网络为基础,首先在Backbone中引入EMA注意力机制,以提高目标检测精度;其次将Neck中的PANFPN网络替换为ASF(Attentional Scale Sequence Fusion)网络,并采用DIoU优化损失函数;在特征匹配算法ORB的基础上,利用RANSAC算法剔除误匹配的点对。通过在KITTI数据集和实际交通场景中的实验,结果表明,在20 m的距离范围内,改进后的YOLOv8s网络对汽车、行人和非机动车3类障碍物的检测mAP(mean average precision)达到了91.1%,提高了4.8%,同时测距的平均误差仅为1.55%。展开更多
基金supported by NSFC Joint Fund with Guangdong under Key Project(U1201258)National Natural Science foundation of China(61402261+3 种基金6130308861572286)the scientific research foundation of Shandong Province of Outstanding Young Scientist Award(BS2013DX048)Shandong Ji’nan Science and Technology Development Project(201202015)
文摘Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art color- based saliency detection methods.
基金Project(40971219)supported by the Natural Science Foundation of ChinaProjects(201121202020005,T201221207)supported by the Fundamental Research Fund for the Central Universities,China
文摘A novel cast shadow detection approach was proposed.A stereo vision system was used to capture images instead of traditional single camera.It was based on an assumption that cast shadows were on a special plane.The image obtained from one camera was inversely projected to the plane and then transformed to the view from another camera.The points on the plane shared the same position between original image and the transformed image.As a result,the cast shadows can be detected.In order to improve the efficiency of cast shadow detection and decrease computational complexity,the obvious object areas in CIELAB color space were removed and the potential shadow areas were obtained.Experimental results demonstrate that the proposed approach can detect cast shadows accurately even under various illuminations.
基金supported by the National Natural Science Foundation of China(No.60605023,60775048)Specialized Research Fund for the Doctoral Program of Higher Education(No.20060141006)
文摘An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points (GCPs) is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.
文摘The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.
基金supported by the National Key Research and Development Program of China(Nos.SQ2022YFB3900026 and 2022YFB3903305)supported by the Leading Talents of Guangdong Pearl River Talent Program(No.2021CX02S024)the Guangdong S&T programme(No.2024B1212050011).
文摘Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar(InSAR),fail to accurately capture understory terrain due to limited penetration capabilities,resulting in elevation overestimation in densely vegetated areas.While airborne light detection and ranging(LiDAR)can provide high-accuracy DTMs,its limited spatial coverage and high acquisition cost hinder large-scale applications.Thus,there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models(DSMs).In this study,we propose a simple,interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation(GEDI)and Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)to construct a tree height surface model,which is then subtracted from the stereo-derived DSM to generate the final DTM.By directly incorporating LiDAR constraints,the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs,ensuring ease of implementation and broad applicability.In contrast to machine learning-based terrain modeling methods,which are often prone to overfitting and data bias,the proposed approach is simple,interpretable,and robust across diverse forested landscapes.The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model(DEM)and the forest and buildings removed DEM(FABDEM),a global bare-earth elevation model corrected for vegetation bias.The results indicate that the proposed DTM consistently outperforms the Copernicus DEM(CopDEM)and achieves accuracy comparable to FABDEM.In addition,its finer spatial resolution of 1 m,compared to the 30 m resolution of FABDEM,allows for more detailed terrain representation and better capture of fine-scale variation.This advantage is most pronounced in gently to moderately sloped areas,where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM.The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints,offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable.To illustrate the potential utility of the proposed DTM,we applied it to a fire risk mapping application based on topographic parameters such as slope,aspect,and elevation.This case highlights how improved terrain representation can support geospatial hazard assessments.
基金supported by National Natural Science Foundation of China-Youth Program(No.62303420)。
文摘Electrical connectors are core functional components in aerospace electrical systems.Pin retraction may lead to signal transmission interruption and even system failure,directly affecting the reliability of electrical equipment and causing incalculable consequences.We propose a high-precision pin-retraction detection method that integrates binocular stereo vision with a multi-constrained optimization matching algorithm,aiming to achieve universal recognition of pins across different connector models and robust detection of pin retraction in complex scenarios.In this study,the Delaunay triangulation algorithm is employed to eliminate the misidentified pins from the template matching algorithm.Furthermore,the pin recognition rate is enhanced to nearly 99.75%,and the accuracy of pin center positioning is significantly improved by integrating a contour fitting and positioning algorithm for pin points.Subsequently,the binocular matching of pins is achieved by combining probabilistic epipolar constraints with geometric constraints,thereby completing the threedimensional reconstruction of pin points.The Euclidean distance from the three-dimensional pin points to the reference plane is calculated as the pin retraction amount,enabling the quantitative measurement of pin retraction amount.Through the design of multiple experiments for measuring the pin retraction of different-type electrical connectors and the analysis of the results using the Kullback-Leibler(K-L)divergence,it is demonstrated that the system’s measurement accuracy is superior to 0.05 mm,with an repeatability error of less than 0.035 mm.The effectiveness of the proposed pin-retraction detection method is thus verified,and the detection efficiency over manual operations is greatly enhanced to meet the actual industrial inspection requirements.
文摘为了提升汽车辅助系统对前方障碍物的检测效果并进一步获取精确的距离信息,文章提出了一种基于改进YOLOv8s的交通场景障碍物检测与双目测距方法。该方法以YOLOv8s(You Only Look Once v8s)网络为基础,首先在Backbone中引入EMA注意力机制,以提高目标检测精度;其次将Neck中的PANFPN网络替换为ASF(Attentional Scale Sequence Fusion)网络,并采用DIoU优化损失函数;在特征匹配算法ORB的基础上,利用RANSAC算法剔除误匹配的点对。通过在KITTI数据集和实际交通场景中的实验,结果表明,在20 m的距离范围内,改进后的YOLOv8s网络对汽车、行人和非机动车3类障碍物的检测mAP(mean average precision)达到了91.1%,提高了4.8%,同时测距的平均误差仅为1.55%。
