The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resoluti...The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.展开更多
为探究不同反应条件对MOFs微观结构和宏观性能的影响,在不同反应条件下,以硫酸铜(CuSO_(4)·5H_(2)O)和6-(吡啶-3′-基)间苯二甲酸(H_(2)L)为材料,制备了2种金属有机框架材料(MOFs){[Cu_(2)(L)_(2)·C _(2) H _(3) N]}n(1)和{[C...为探究不同反应条件对MOFs微观结构和宏观性能的影响,在不同反应条件下,以硫酸铜(CuSO_(4)·5H_(2)O)和6-(吡啶-3′-基)间苯二甲酸(H_(2)L)为材料,制备了2种金属有机框架材料(MOFs){[Cu_(2)(L)_(2)·C _(2) H _(3) N]}n(1)和{[Cu(HL)_(2)]}n(2),并对合成的金属有机框架材料1和2进行了结构分析、TGA和FTIR表征以及对染料的吸附性能研究。单晶衍射数据分析表明:配合物1和2都属单斜晶系,P 21/c空间群。配合物1是一种基于双核轮桨次级构筑单元[Cu_(2)(COO)_(4)]构筑的三维紧密堆积化合物,配合物2由于配体部分质子化,结构中存在氢键[O_(3)—H…O_(4)],将二维层状网络结构连接成三维结构。配合物1是rtl拓扑类型的网络结构,拓扑符号为(42.610.83);配合物2是sql拓扑类型的网络结构,拓扑符号为(4^(4).6^(2))。2种配合物在相同时间间隔下对不同染料降解性能表明配合物2对水溶液中的孔雀石绿具有一定的化学吸附降解能力。展开更多
基金National Natural Science Foundation of China(No.41871305)National Key Research and Development Program of China(No.2017YFC0602204)+2 种基金Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(No.CUGQY1945)Open Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education and the Fundamental Research Funds for the Central Universities(No.GLAB2019ZR02)Open Fund of Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources,China(No.KF-2020-05-068)。
文摘The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.
