A Comprehensive Review on Tree Detection Methods Using Point Cloud and
Aerial Imagery from Unmanned Aerial Vehicles
- URL: http://arxiv.org/abs/2309.16375v1
- Date: Thu, 28 Sep 2023 12:22:39 GMT
- Title: A Comprehensive Review on Tree Detection Methods Using Point Cloud and
Aerial Imagery from Unmanned Aerial Vehicles
- Authors: Weijie Kuang, Hann Woei Ho, Ye Zhou, Shahrel Azmin Suandi, and Farzad
Ismail
- Abstract summary: This paper focuses on tree detection methods applied to UAV data collected by UAVs.
For the detection methods using images directly, this paper reviews these methods by whether or not to use the Deep Learning (DL) method.
This review could help researchers who want to carry out tree detection on specific forests and for farmers to use UAVs in managing agriculture production.
- Score: 4.362788465317224
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Unmanned Aerial Vehicles (UAVs) are considered cutting-edge technology with
highly cost-effective and flexible usage scenarios. Although many papers have
reviewed the application of UAVs in agriculture, the review of the application
for tree detection is still insufficient. This paper focuses on tree detection
methods applied to UAV data collected by UAVs. There are two kinds of data, the
point cloud and the images, which are acquired by the Light Detection and
Ranging (LiDAR) sensor and camera, respectively. Among the detection methods
using point-cloud data, this paper mainly classifies these methods according to
LiDAR and Digital Aerial Photography (DAP). For the detection methods using
images directly, this paper reviews these methods by whether or not to use the
Deep Learning (DL) method. Our review concludes and analyses the comparison and
combination between the application of LiDAR-based and DAP-based point cloud
data. The performance, relative merits, and application fields of the methods
are also introduced. Meanwhile, this review counts the number of tree detection
studies using different methods in recent years. From our statics, the
detection task using DL methods on the image has become a mainstream trend as
the number of DL-based detection researches increases to 45% of the total
number of tree detection studies up to 2022. As a result, this review could
help and guide researchers who want to carry out tree detection on specific
forests and for farmers to use UAVs in managing agriculture production.
Related papers
- A noisy elephant in the room: Is your out-of-distribution detector robust to label noise? [49.88894124047644]
We take a closer look at 20 state-of-the-art OOD detection methods.
We show that poor separation between incorrectly classified ID samples vs. OOD samples is an overlooked yet important limitation of existing methods.
arXiv Detail & Related papers (2024-04-02T09:40:22Z) - Beyond AUROC & co. for evaluating out-of-distribution detection
performance [50.88341818412508]
Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs.
We propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples.
arXiv Detail & Related papers (2023-06-26T12:51:32Z) - A review of UAV Visual Detection and Tracking Methods [0.0]
There are different techniques that depend on collecting measurements of the position, velocity, and image of the UAV.
The paper is a quick reference for a wide spectrum of methods that are used in the drone detection process.
arXiv Detail & Related papers (2023-06-08T10:48:11Z) - Investigation of UAV Detection in Images with Complex Backgrounds and
Rainy Artifacts [20.20609511526255]
Vision-based object detection methods have been developed for UAV detection.
UAV detection in images with complex backgrounds and weather artifacts like rain has yet to be reasonably studied.
This work also focuses on benchmarking state-of-the-art object detection models.
arXiv Detail & Related papers (2023-05-25T19:54:33Z) - High-Resolution UAV Image Generation for Sorghum Panicle Detection [23.88932181375298]
We present an approach that uses synthetic training images from generative adversarial networks (GANs) for data augmentation to enhance the performance of Sorghum panicle detection and counting.
Our method can generate synthetic high-resolution UAV RGB images with panicle labels by using image-to-image translation GANs with a limited ground truth dataset of real UAV RGB images.
arXiv Detail & Related papers (2022-05-08T20:26:56Z) - Development of Automatic Tree Counting Software from UAV Based Aerial
Images With Machine Learning [0.0]
This study aims to automatically count trees in designated areas on the Siirt University campus from high-resolution images obtained by UAV.
Images obtained at 30 meters height with 20% overlap were stitched offline at the ground station using Adobe Photoshop's photo merge tool.
arXiv Detail & Related papers (2022-01-07T22:32:08Z) - Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today.
We propose a novel deep learning algorithm to automate this aerial person detection (APD) task.
We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
arXiv Detail & Related papers (2021-11-17T21:48:31Z) - Deep Learning for UAV-based Object Detection and Tracking: A Survey [25.34399619170044]
Unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS)
Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks.
This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods.
arXiv Detail & Related papers (2021-10-25T04:43:24Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - Potato Crop Stress Identification in Aerial Images using Deep
Learning-based Object Detection [60.83360138070649]
The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks.
The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level.
Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
arXiv Detail & Related papers (2021-06-14T21:57:40Z) - Perceiving Traffic from Aerial Images [86.994032967469]
We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images.
We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
arXiv Detail & Related papers (2020-09-16T11:37:43Z)
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.