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Road Traffic Monitoring from aerial images Using Template Matching and Invariant Features

Computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Detection of collapsed buildings with the aerial images captured from UAV

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Science China(Information Sciences) 2016年第3期59卷 22-36页

作者： Chunsheng HUA Juntong QI Hong SHANG Weijian HU Jianda HAN State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences National Earthquake Response Support Service

In this paper, we present a method of detecting the collapsed buildings with the aerial images which are captured by an unmanned aerial vehicle(UAV) for the postseismic evaluation. Different from the conventional methods that apply the satellite images or the high-altitude UAV for the coarse disaster evaluation over large area, the purpose of this work is to achieve the accurate detection of collapsed buildings in small area from low altitude. By combining the motion and appearance features of collapsed buildings extracted from successive aerial images, each pixel in the input image will be measured by a statistical method where the background pixels will be penalized and the ones of collapsed buildings will be assigned with high *** candidates of collapsed buildings will be established by integrating the extracted feature points into local groups with the online clustering algorithm. To reduce the false alarm caused by the complex background noise,each predicted candidate will be further verified by the temporal tracking framework where both the trajectory and the appearance of a candidate will be measured. The candidate of collapsed buildings that can survive through long time will be considered as true positive, otherwise rejected as a false alarm. Through extensive experiments, the efficiency and the effectiveness of proposed algorithm have been proved.

关键词： collapsed buildings aerial images UAV online detection temporal tracking

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Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

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Defence Technology（防务技术） 2021年第4期17卷 1531-1541页

作者： Lei Fu Wen-bin Gu Wei Li Liang Chen Yong-bao Ai Hua-lei Wang College of Field Engineering Army Engineering University of PLANanjing210007China 93182 troops People's Liberation ArmyShenyang110000China 93033 troops People's Liberation ArmyShenyang110000China Automobile NCO Academy Army Military Transportation UniversityBengbu233011China

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 ***,the feature pyramid is integrated into the single-stage object detection framework to ensure real-time *** order to validate the effectiveness of the proposed algorithm,experiments are conducted on four *** the PASCAL VOC dataset,the proposed algorithm achieves the mean average precision(mAP)of 85.4 on the VOC 2007 test *** regard to the detection in optical remote sensing(DIOR)dataset,the proposed algorithm achieves 73.9 *** vehicle detection in aerial imagery(VEDAI)dataset,the detection accuracy of small land vehicle(slv)targets reaches 97.4 *** unmanned aerial vehicle detection and tracking(UAVDT)dataset,the proposed BPMFPN Det achieves the mAP of *** with the previous state-of-the-art methods,the results obtained by the proposed algorithm are more *** 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.

关键词： aerial images Object detection Feature pyramid networks Multi-scale feature fusion Swarm UAVs

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Improved seeded region growing for detection of water bodies in aerial images

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Geo-Spatial Information Science 2016年第1期19卷 1-8页

作者： Jun Pan Mi Wang State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan University129 Luoyu RoadWuhan 430079China Collaborative Innovation Center for Geospatial Technology Wuhan University129 Luoyu RoadWuhan 430079China

In aerial images,near-specular and specular reflection often appear in water *** often lead to irregular brightness or color changes in water bodies and even produce hot spots,harmful to radiometric ***,water bodies must be eliminated when calculating radiometric differences during radiometric normalization of aerial *** this paper,a simple method to detect water bodies in aerial images based on texture features is presented,an improved seeded region growing(SRG)method.A texture feature is calculated using the relative standard deviation index(RSDI)and a coarse-to-fine procedure is *** proposed method includes a multiple partition strategy and a refinement in gradient image that improves the reliability and accuracy of water body *** fusing water bodies detected in multiple images,hot spots in these water bodies are also *** validate the feasibility and effectiveness of the proposed method.

关键词： aerial images water specular reflection hot spots radiometric normalization

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Automatic detection and evaluation of sugarcane planting rows in aerial images

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Information Processing in Agriculture 2023年第3期10卷 400-415页

作者： Bruno Moraes Rocha Afonso Ueslei da Fonseca Helio Pedrini Fabrızzio Soares Instituto de Informa´tica Universidade Federal de Goia´sGoiaˆnia 74690-900GOBrazil Institute of Computing University of CampinasCampinas 13083-852SPBrazil

Sugarcane planting is an important and growing activity in ***,several techniques have been developed over the years to maximize crop productivity and profit,amongst them,processing of sugarcane field *** this sense,this research aims to identify and analyze crop rows and measure their gaps from aerial images of sugarcane *** this,a small Remotely Piloted Aircraft captured the images,generating orthomosaics of the areas for ***,each orthomosaic is classified with the K-Nearest Neighbor algorithm to segment regions of *** row orientation is estimated using the RGB gradient *** operations and computational geometry models are then used to detect and map rows and gaps along the planting row *** evaluate the results,crop rows are mapped and compared to manually taken *** technique obtained an error smaller than 2%when compared to gap length in crop rows from an orthomosaic with the area of 8.05 ha(ha).The proposed approach can map the positioning of the automatically generated row segments appropriately onto manually created ***,our method also achieved similar results when confronted with a manual technique for differing growth stages(40 and 80 days after harvest)of the sugarcane *** proposed method presents a great potential to be adopted in sugarcane planting monitoring。

关键词： Sugarcane Planting Rows aerial images Remotely Piloted Aircraft

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Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in aerial images

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2237-2265页

作者： Ying Li Guanghong Gong Dan Wang Ni Li School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191China Shenyuan Honors College Beihang UniversityBeijing100191China State Key Laboratory of Virtual Reality Technology and Systems Beijing100191China

There are two types of methods for image *** is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic *** other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate *** of them cannot generate entire segmentation *** order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is *** method can precisely semantic segment individual objects in large-size aerial images with limited GPU *** includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different *** object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and *** experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning *** work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.

关键词： Semantic segmentation aerial images composite method traditional image processing deep learning

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An Accurate and Real-time Method of Self-blast Glass Insulator Location Based on Faster R-CNN and U-net with aerial images

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CSEE Journal of Power and Energy Systems 2019年第4期5卷 474-482页

作者： Zenan Ling Dongxia Zhang Robert C.Qiu Zhijian Jin Yuhang Zhang Xing He Haichun Liu Department of Electrical Engineering Center for Big Data and Artificial IntelligenceState Energy Smart Grid Research and Development CenterShanghai Jiao Tong UniversityShanghai 200240China China Electric Power Research Institute Beijing 100192China Department of Electrical and Computer Engineering Tennessee Technological UniversityCookevilleTN 38505USA

This paper proposes a new deep learning framework for the location of broken insulators(in particular the self-blast glass insulator)in aerial *** address the broken insulators location problem in a low signal-noise-ratio(SNR)*** deal with two modules:1)object detection based on Faster R-CNN,and 2)classification of pixels based on *** the first time,our paper combines the above two *** combination is motivated as follows:Faster R-CNN is used to improve SNR,while the U-net is used for classification of pixels.A diverse aerial image set measured by a power grid in China is tested to validate the proposed ***,a comparison is made among different methods and the result shows that our approach is accurate in real time.

关键词： aerial images deep learning faster R-CNN insulators location real-time U-net

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Acquisition of Directional Parameters in aerial images Based on DEM Data

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Geo-Spatial Information Science 2005年第1期8卷 23-27页

作者： LIPingxiang YUJie BIANFuling 不详 professor StateKeyLaboratoryofInformationEngineeringinSurveyingMappingandRemoteSensingWuhanUniversity.129LuoyuRoad.Wuhan430079China.

This paper develops a method which can be used to assist aerial navigation by determining the spatial position and posture of the aerial photographic plane. After the method, aerial images match known DEM to capture the spatial position and posture. Some aerial images and terrain data are used to testify our method. Compared with those of analytic and stereo mappers, the results by our method are correspondent to real measurements well.

关键词： aerial navigation aerial images DEM data

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CHANGE DETECTION FROM aerial images ACQUIRED IN DIFFERENT DURATIONS

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Geo-Spatial Information Science 1999年第1期2卷 16-20页

作者： Zhang Jianqing Zhang Zuxun Fang Zhen Fan Hong National Laboratory for Information Engineering in Surveying Mapping and Remote Sensing WTUSM Wuhan China

Because of quick development of cities, the update of urban GIS data is very important. Change detection is the base of automatic or semi-automatic data update. One way of change detections in urban area is based on old and new aerial images acquired in different durations. The corresponding theory and experiments are introduced and analyzed in this paper. The main procedure includes four stages. The new and old images have to be registered firstly. Then image matching, based on the maximum correlation coefficient, is performed between registered images after the low contrast areas have been removed. The regions with low matching quality are extracted as candidate changed areas. Thirdly, the Gaussian-Laplacian operator is used to detect edges in candidate changed areas on both the registered images, and the straight lines are detected by Hough transformation. Finally, the changed houses and roads can be detected on the basis of straight line matching in candidate changed areas between registered images. Some experimental results show that the method introduced in this paper is effective.

关键词： change detection aerial images urban

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An Effective Algorithm for aerial images Fusion

An Effective Algorithm for Aerial Images Fusion

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第24届中国控制与决策会议

作者： Wang Yajie, Sun Yuxia, Liu Cheng, Li Fei Engineering Training Center, Shenyang Aerospace University, Shenyang, 110136

An Effective low-light-television （LLTV） and forward-looking-infrared （FLIR） aerial images fusion method based on dual-tree complex wavelet transform （DT-CWT） is presented for flight navigation. The deficiencies of traditional discrete wavelet transform （DWT） based algorithms in characteristics representation are very obvious. The DT-CWT is suitable to image fusion processing because of its shift invariance and directional selectivity. It is preferable to traditional DWT. The simple and effective fusion rules are also putted forward. The high frequency parts use absolute value as measurement, and the low frequency parts use average after DT-CWT of source images. Comparing with the other seven methods, the experimental results and the evaluating statistics demonstrate that the proposed fusion algorithm is effective.

关键词： Image Fusion aerial images Dual-tree Complex Wavelet

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