scholarly journals Automatic Garbage Scattered Area Detection with Data Augmentation and Transfer Learning in SUAV Low-Altitude Remote Sensing Images

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Tengfei You ◽  
Weiyang Chen ◽  
Haifeng Wang ◽  
Yang Yang ◽  
Xinang Liu
Keyword(s):  
Remote Sensing ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Traditional Approach ◽  
Nature Reserves ◽  
Ecological Environment ◽  
Future Research ◽  
Method Transfer ◽  
Aerial Vehicle ◽  
Low Altitude

Cleaning up the garbage timely plays an important role in protecting the ecological environment of nature reserves. The traditional approach adopts manual patrol and centralized cleaning to clean up garbage, which is inefficient. In order to protect the ecological environment of nature reserves, this paper proposes an automatic garbage scattered area detection (GSAD) model based on the state-of-the-art deep learning EfficientDet method, transfer learning, data augmentation, and image blocking. The main contributions of this paper are (1) we build a garbage sample dataset based on small unmanned aerial vehicle (SUAV) low-altitude remote sensing and (2) we propose a novel data augmentation approach based on garbage scattered area detection and (3) this paper establishes a model (GSAD) for garbage scattered area detection based on data augmentation, transfer learning, and image blocking and gives future research directions. Experimental results show that the GSAD model can achieve the F1-score of 95.11% and average detection time of 1.096 s.

scholarly journals Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 66
Author(s):  
Rahee Walambe ◽  
Aboli Marathe ◽  
Ketan Kotecha
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Test Time ◽  
Complex Task ◽  
Open Domain ◽  
End User ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Voting Strategy

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions.

scholarly journals Controller for a Low-Altitude Fixed-Wing UAV on an Embedded System to Assess Specific Environmental Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Johannes von Eichel-Streiber ◽  
Christoph Weber ◽  
Jesús Rodrigo-Comino ◽  
Jens Altenburg
Keyword(s):  
Embedded System ◽  
Environmental Conditions ◽  
Plant Density ◽  
Future Research ◽  
Digital Elevation ◽  
Small Uav ◽  
Aerial Vehicle ◽  
Low Costs ◽  
Low Altitude ◽  
Elevation Model

The use of an appropriate sensor on an unmanned aerial vehicle (UAV) is vital to assess specific environmental conditions successfully. In addition, technicians and scientists also appreciate a platform to carry the sensors with some advantages such as the low costs or easy pilot management. However, extra requirements like a low-altitude flight are necessary for special applications such as plant density or rice yield. A rotary UAV matches this requirement, but the flight endurance is too short for large areas. Therefore, in this article, a fixed-wing UAV is used, which is more appropriate because of its longer flight endurance. It is necessary to develop an own controller system to use special sensors such as Lidar or Radar on the platform as a multifunctionality system. Thereby, these sensors are used to generate a digital elevation model and also as a collision avoidance sensor at the same time. To achieve this goal, a small UAV was equipped with a hardware platform including a microcontroller and sensors. After testing the system and simulation, the controller was converted into program code to implement it on the microcontroller. After that, several real flights were performed to validate the controller and sensors. We demonstrated that the system is able to work and match the high requirements for future research.

scholarly journals Advantage and Application of Unmanned Aerial Vehicle Remote Sensing in Engineering Survey

2020 ◽  
Vol 9 (1) ◽  
pp. 22
Author(s):  
Qing Yan
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Good Imaging ◽  
Collection Data ◽  
Aerial Vehicle ◽  
Good Measurement ◽  
Engineering Survey ◽  
Low Altitude ◽  
High Work ◽  
Measurement Area

<p>Unmanned aerial vehicle remote sensing can fly at low altitude, shoot high-pixel, good-imaging images, and quickly acquire the geographic characteristics of the measurement area. It has the advantages of high periodic service quality, high work efficiency, wide monitoring range and good monitoring effect when applied in engineering survey. Unmanned aerial vehicle remote sensing has played a great role in the field of engineering survey, which can achieve data collection, data analysis and other work. At the same time, it can also obtain geographic information of the survey area in harsh environment, which has fast measurement speed and good measurement accuracy, and has very good application and development value.</p>

scholarly journals Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning

2020 ◽  
Vol 12 (5) ◽  
pp. 752 ◽  
Author(s):  
Heng Lu ◽  
Lei Ma ◽  
Xiao Fu ◽  
Chao Liu ◽  
Zhi Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Deep Learning ◽  
Transfer Learning ◽  
High Efficiency ◽  
Object Oriented ◽  
Histograms Of Oriented Gradients ◽  
Low Altitude ◽  
Uav Images ◽  
Landslide Disaster

How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed.

scholarly journals A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China

2021 ◽  
Vol 13 (6) ◽  
pp. 1221
Author(s):  
Haidong Zhang ◽  
Lingqing Wang ◽  
Ting Tian ◽  
Jianghai Yin
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Current Application ◽  
Rapid Acquisition ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
Future Work

Precision agriculture relies on the rapid acquisition and analysis of agricultural information. An emerging method of agricultural monitoring is unmanned aerial vehicle low-altitude remote sensing (UAV-LARS), which possesses significant advantages of simple construction, strong mobility, and high spatial-temporal resolution with synchronously obtained image and spatial information. UAV-LARS could provide a high degree of overlap between X and Y during key crop growth periods that is currently lacking in satellite and remote sensing data. Simultaneously, UAV-LARS overcomes the limitations such as small scope of ground platform monitoring. Overall, UAV-LARS has demonstrated great potential as a tool for monitoring agriculture at fine- and regional-scales. Here, we systematically summarize the history and current application of UAV-LARS in Chinese agriculture. Specifically, we outline the technical characteristics and sensor payload of the available types of unmanned aerial vehicles and discuss their advantages and limitations. Finally, we provide suggestions for overcoming current limitations of UAV-LARS and directions for future work.

scholarly journals Preliminary analysis of the forest health state based on multispectral images acquired by Unmanned Aerial Vehicle

2015 ◽  
Vol 57 (3) ◽  
pp. 138-144 ◽  
Author(s):  
Paweł Czapski ◽  
Mariusz Kacprzak ◽  
Jan Kotlarz ◽  
Karol Mrowiec ◽  
Katarzyna Kubiak ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Forest Health ◽  
Growing Season ◽  
Landsat 8 ◽  
Health State ◽  
Multispectral Images ◽  
Landsat 8 Oli ◽  
Aerial Vehicle ◽  
Low Altitude

Abstract The main purpose of this publication is to present the current progress of the work associated with the use of a lightweight unmanned platforms for various environmental studies. Current development in information technology, electronics and sensors miniaturisation allows mounting multispectral cameras and scanners on unmanned aerial vehicle (UAV) that could only be used on board aircraft and satellites. Remote Sensing Division in the Institute of Aviation carries out innovative researches using multisensory platform and lightweight unmanned vehicle to evaluate the health state of forests in Wielkopolska province. In this paper, applicability of multispectral images analysis acquired several times during the growing season from low altitude (up to 800m) is presented. We present remote sensing indicators computed by our software and common methods for assessing state of trees health. The correctness of applied methods is verified using analysis of satellite scenes acquired by Landsat 8 OLI instrument (Operational Land Imager).

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