Automatic detection of aerial survey ground control points based on Yolov5-OBB

• URL: http://arxiv.org/abs/2303.03041v1
• Date: Mon, 6 Mar 2023 11:13:23 GMT
• Title: Automatic detection of aerial survey ground control points based on Yolov5-OBB
• Authors: Cheng Chuanxiang, Yang Jia, Wang Chao, Zheng Zhi, Li Xiaopeng, Dong Di, Chang Mengxia, Zhuang Zhiheng
• Abstract summary: We propose a solution that uses a deep learning-based architecture, YOLOv5-OBB, combined with a confidence threshold filtering algorithm and an optimal ranking algorithm. We applied our proposed method to a dataset collected by DJI Phantom 4 Pro drone.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The use of ground control points (GCPs) for georeferencing is the most common strategy in unmanned aerial vehicle (UAV) photogrammetry, but at the same time their collection represents the most time-consuming and expensive part of UAV campaigns. Recently, deep learning has been rapidly developed in the field of small object detection. In this letter, to automatically extract coordinates information of ground control points (GCPs) by detecting GCP-markers in UAV images, we propose a solution that uses a deep learning-based architecture, YOLOv5-OBB, combined with a confidence threshold filtering algorithm and an optimal ranking algorithm. We applied our proposed method to a dataset collected by DJI Phantom 4 Pro drone and obtained good detection performance with the mean Average Precision (AP) of 0.832 and the highest AP of 0.982 for the cross-type GCP-markers. The proposed method can be a promising tool for future implementation of the end-to-end aerial triangulation process.

Related papers

• SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection [59.868772767818975]
  We propose a simple yet effective Semi-supervised Oriented Object Detection method termed SOOD++. Specifically, we observe that objects from aerial images are usually arbitrary orientations, small scales, and aggregation. Extensive experiments conducted on various multi-oriented object datasets under various labeled settings demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-07-01T07:03:51Z)
• Clustering-based Learning for UAV Tracking and Pose Estimation [0.0]
  This work develops a clustering-based learning detection approach, CL-Det, for UAV tracking and pose estimation using two types of LiDARs. We first align the timestamps of Livox Avia data and LiDAR 360 data and then separate the point cloud of objects of interest (OOIs) from the environment. The proposed method shows competitive pose estimation performance and ranks 5th on the final leaderboard of the CVPR 2024 UG2+ Challenge.
  arXiv  Detail & Related papers  (2024-05-27T06:33:25Z)
• FlightScope: A Deep Comprehensive Review of Aircraft Detection Algorithms in Satellite Imagery [2.9687381456164004]
  This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. This research encompasses an array of methodologies including YOLO versions 5 and 8, Faster RCNN, CenterNet, RetinaNet, RTMDet, and DETR, all trained from scratch. YOLOv5 emerges as a robust solution for aerial object detection, underlining its importance through superior mean average precision, Recall, and Intersection over Union scores.
  arXiv  Detail & Related papers  (2024-04-03T17:24:27Z)
• V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection [73.37781484123536]
  We introduce a highly performant 3D object detector for point clouds using the DETR framework. To address the limitation, we introduce a novel 3D Relative Position (3DV-RPE) method. We show exceptional results on the challenging ScanNetV2 benchmark.
  arXiv  Detail & Related papers  (2023-08-08T17:14:14Z)
• Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing [77.34726150561087]
  This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
  arXiv  Detail & Related papers  (2022-11-02T11:10:42Z)
• Anchor-free Oriented Proposal Generator for Object Detection [59.54125119453818]
  Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. We propose a novel Anchor-free Oriented Proposal Generator (AOPG) that abandons the horizontal boxes-related operations from the network architecture.
  arXiv  Detail & Related papers  (2021-10-05T10:45:51Z)
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach [93.67588414950656]
  In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment. We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
  arXiv  Detail & Related papers  (2021-07-23T03:33:29Z)
• Planetary UAV localization based on Multi-modal Registration with Pre-existing Digital Terrain Model [0.5156484100374058]
  We propose a multi-modal registration based SLAM algorithm, which estimates the location of a planet UAV using a nadir view camera on the UAV. To overcome the scale and appearance difference between on-board UAV images and pre-installed digital terrain model, a theoretical model is proposed to prove that topographic features of UAV image and DEM can be correlated in frequency domain via cross power spectrum. To test the robustness and effectiveness of the proposed localization algorithm, a new cross-source drone-based localization dataset for planetary exploration is proposed.
  arXiv  Detail & Related papers  (2021-06-24T02:54:01Z)
• Deep Learning based Multi-Modal Sensing for Tracking and State Extraction of Small Quadcopters [3.019035926889528]
  This paper proposes a multi-sensor based approach to detect, track, and localize a quadcopter unmanned aerial vehicle (UAV) Specifically, a pipeline is developed to process monocular RGB and thermal video (captured from a fixed platform) to detect and track the UAV in our FoV. A 2D planar lidar is used to allow conversion of pixel data to actual distance measurements, and thereby enable localization of the UAV in global coordinates.
  arXiv  Detail & Related papers  (2020-12-08T23:59:48Z)
• A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds [54.73161039445703]
  We propose a novel self-training approach that enables a typical object detector trained only with point-level annotations. During training, we utilize the available point annotations to supervise the estimation of the center points of objects. Experimental results show that our approach significantly outperforms state-of-the-art point-supervised methods under both detection and counting tasks.
  arXiv  Detail & Related papers  (2020-07-25T02:14:42Z)
• Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection [36.79380276028116]
  We study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection.
  arXiv  Detail & Related papers  (2020-05-05T20:39:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.