This work presented a new drone-based face detection dataset Drone LAMS in order to solve issues of low performance of drone-based face detection in scenarios such as large angles which was a predominant working condition when a drone flies high. The proposed dataset captured images from 261 videos with over 43k annotations and 4.0k images with pitch or yaw angle in the range of -90° to 90°. Drone LAMS showed significant improvement over currently available drone-based face detection datasets in terms of detection performance, especially with large pitch and yaw angle. Detailed analysis of how key factors, such as duplication rate, annotation method, etc., impact dataset performance was also provided to facilitate further usage of a drone on face detection.
Drone Referring Localization: An Efficient Heterogeneous Spatial Feature Interaction Method For UAV Self-Localization
School of Automation, Southeast University（东南大学自动化学院）；Unmanned System Application Technology Research Institute, China Jiliang University（中国计量大学无人系统应用技术研究院）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；School of Computer Science, Southeast University（东南大学计算机科学学院）
Ulsan National Institute of Science and Technology（蔚山科学技术院）；Korea Institute of Science and Technology（韩国科学技术研究院）
The robot market has been growing significantly and is expected to become 1.5 times larger in 2024 than what it was in 2019. Robots have attracted attention of security companies thanks to their mobility. These days, for security robots, unmanned aerial vehicles (UAVs) have quickly emerged by highlighting their advantage: they can even go to any hazardous place that humans cannot access. For UAVs, Drone has been a representative model and has several merits to consist of various sensors such as high-resolution cameras. Therefore, Drone is the most suitable as a mobile surveillance robot. These…
The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. Since this inspection is a slow, tedious and error-prone job for humans, we propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We experiment with model architecture selection, online data augmentation, transfer learning, image tiling and several other techniques to improve the test performance of our method. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions. The AIR detector demonst…
Pathfinding in Euclidean space is a common problem faced in robotics and computer games. For long-distance navigation on the surface of the earth or in outer space however, approximating the geometry as Euclidean can be insufficient for real-world applications such as the navigation of spacecraft, aeroplanes, drones and ships. This article describes an any-angle pathfinding algorithm for calculating the shortest path between point pairs over the surface of a sphere. Introducing several novel adaptations, it is shown that Anya as described by (Harabor & Grastien, 2013) for Euclidean space can b…
A2Seek: Towards Reasoning-Centric Benchmark for Aerial Anomaly Understanding
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory（重庆脑科学与智能研究院，广阳湾实验室）
Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge
Shanghai Jiao Tong University（上海交通大学）；JITRI（江苏集萃智能制造技术研究所有限公司）；Dalian University of Technology（大连理工大学）；University of Electronic Science and Technology of China（电子科技大学）；The University of Tokyo（东京大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
RemoteDet-Mamba: A Hybrid Mamba-CNN Network for Multi-modal Object Detection in Remote Sensing Images
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China（计算机科学学院，北京邮电大学）；School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China（电子工程学院，北京邮电大学）
Advancements in deep neural networks have contributed to near perfect results for many computer vision problems such as object recognition, face recognition and pose estimation. However, human action recognition is still far from human-level performance. Owing to the articulated nature of the human body, it is challenging to detect an action from multiple viewpoints, particularly from an aerial viewpoint. This is further compounded by a scarcity of datasets that cover multiple viewpoints of actions. To fill this gap and enable research in wider application areas, we present a multi-viewpoint o…
MO R-CNN: Multispectral Oriented R-CNN for Object Detection in Remote Sensing Image
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息工程学院）；School of Mechanical Engineering and Automation, Fuzhou University（福州大学机械工程与自动化学院）；Yunnan Key Laboratory of Software Engineering, Yunnan University（云南软件工程重点实验室）
针对多光谱定向目标检测中模态差异与计算开销问题，提出轻量级框架 MO R-CNN，通过异构特征提取、单模态监督和条件标签融合提升检测性能。
