When a vehicle travels in urban areas, onboard global positioning system （GPS） signals may be obstructed by high-rise buildings and thereby cannot provide accurate positions. It is proposed to perform localization b...When a vehicle travels in urban areas, onboard global positioning system （GPS） signals may be obstructed by high-rise buildings and thereby cannot provide accurate positions. It is proposed to perform localization by registering ground images to a 2D building boundary map which is generated from aerial images. Multilayer feature graphs （MFG） is employed to model building facades from the ground images. MFG was reported in the previous work to facilitate the robot scene understand- ing in urhan areas. By constructing MFG, the 2D/3D positions of features can be obtained, inclu- cling line segments, ideal lines, and all primary vertical planes. Finally, a voting-based feature weighted localization method is developed based on MFGs and the 2D building boundary map. The proposed method has been implemented and validated in physical experiments. In the proposed ex- periments, the algorithm has achieved an overall localization accuracy of 2.2m, which is better than commercial GPS working in open environments.展开更多
When detecting objects in Unmanned Aerial Vehicle(UAV)taken images,large number of objects and high proportion of small objects bring huge challenges for detection algorithms based on the You Only Look Once(YOLO)frame...When detecting objects in Unmanned Aerial Vehicle(UAV)taken images,large number of objects and high proportion of small objects bring huge challenges for detection algorithms based on the You Only Look Once(YOLO)framework,rendering them challenging to deal with tasks that demand high precision.To address these problems,this paper proposes a high-precision object detection algorithm based on YOLOv10s.Firstly,a Multi-branch Enhancement Coordinate Attention(MECA)module is proposed to enhance feature extraction capability.Secondly,a Multilayer Feature Reconstruction(MFR)mechanism is designed to fully exploit multilayer features,which can enrich object information as well as remove redundant information.Finally,an MFR Path Aggregation Network(MFR-Neck)is constructed,which integrates multi-scale features to improve the network's ability to perceive objects of var-ying sizes.The experimental results demonstrate that the proposed algorithm increases the average detection accuracy by 14.15%on the Vis Drone dataset compared to YOLOv10s,effectively enhancing object detection precision in UAV-taken images.展开更多
基金Supported by the National High Technology Research and Development Program of China(No.2012AA041403)National Natural Science Foundation of China(No.60905061,61305107)+1 种基金the Fundamental Research Funds for the Central Universities(No.ZXH2012N003)the Scientific Research Funds for Civil Aviation University of China(No.2012QD23x)
文摘When a vehicle travels in urban areas, onboard global positioning system （GPS） signals may be obstructed by high-rise buildings and thereby cannot provide accurate positions. It is proposed to perform localization by registering ground images to a 2D building boundary map which is generated from aerial images. Multilayer feature graphs （MFG） is employed to model building facades from the ground images. MFG was reported in the previous work to facilitate the robot scene understand- ing in urhan areas. By constructing MFG, the 2D/3D positions of features can be obtained, inclu- cling line segments, ideal lines, and all primary vertical planes. Finally, a voting-based feature weighted localization method is developed based on MFGs and the 2D building boundary map. The proposed method has been implemented and validated in physical experiments. In the proposed ex- periments, the algorithm has achieved an overall localization accuracy of 2.2m, which is better than commercial GPS working in open environments.
基金co-supported by the National Natural Science Foundation of China(No.62103190)the Natural Science Foundation of Jiangsu Province,China(No.BK20230923)。
文摘When detecting objects in Unmanned Aerial Vehicle(UAV)taken images,large number of objects and high proportion of small objects bring huge challenges for detection algorithms based on the You Only Look Once(YOLO)framework,rendering them challenging to deal with tasks that demand high precision.To address these problems,this paper proposes a high-precision object detection algorithm based on YOLOv10s.Firstly,a Multi-branch Enhancement Coordinate Attention(MECA)module is proposed to enhance feature extraction capability.Secondly,a Multilayer Feature Reconstruction(MFR)mechanism is designed to fully exploit multilayer features,which can enrich object information as well as remove redundant information.Finally,an MFR Path Aggregation Network(MFR-Neck)is constructed,which integrates multi-scale features to improve the network's ability to perceive objects of var-ying sizes.The experimental results demonstrate that the proposed algorithm increases the average detection accuracy by 14.15%on the Vis Drone dataset compared to YOLOv10s,effectively enhancing object detection precision in UAV-taken images.
