Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles
The paper discusses the problem of detecting emission sources in a low buildings area using unmanned aerial vehicles. The problem was analyzed, and methods of solving it were presented. Various data acquisition scenarios and their impact on the feasibility of the task were analyzed. A method for det...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/4/2235 |
| _version_ | 1797618241138327552 |
|---|---|
| author | Marek Szczepański |
| author_facet | Marek Szczepański |
| author_sort | Marek Szczepański |
| collection | DOAJ |
| description | The paper discusses the problem of detecting emission sources in a low buildings area using unmanned aerial vehicles. The problem was analyzed, and methods of solving it were presented. Various data acquisition scenarios and their impact on the feasibility of the task were analyzed. A method for detecting smoke objects over buildings using stationary video sequences acquired with a drone in hover with the camera in the nadir position is proposed. The method uses differential frame information from stabilized video sequences and the YOLOv7 classifier. A convolutional network classifier was used to detect the roofs of buildings, using a custom training set adapted to the type of data used. Such a solution, although quite effective, is not very practical for the end user, but it enables the automatic generation of a comprehensive training set for classifiers based on deep neural networks. The effectiveness of such a solution was tested for the latest version of the YOLOv7 classifier. The tests proved the effectiveness of the described method, both for single images and video sequences. In addition, the obtained classifier correctly recognizes objects for sequences that do not meet some of the initial assumptions, such as the angle of the camera capturing the image. |
| first_indexed | 2024-03-11T08:10:18Z |
| format | Article |
| id | doaj.art-e20e407f3eae4c50a4ad79ae4325fe14 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:18Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e20e407f3eae4c50a4ad79ae4325fe142023-11-16T23:12:07ZengMDPI AGSensors1424-82202023-02-01234223510.3390/s23042235Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial VehiclesMarek Szczepański0Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, PolandThe paper discusses the problem of detecting emission sources in a low buildings area using unmanned aerial vehicles. The problem was analyzed, and methods of solving it were presented. Various data acquisition scenarios and their impact on the feasibility of the task were analyzed. A method for detecting smoke objects over buildings using stationary video sequences acquired with a drone in hover with the camera in the nadir position is proposed. The method uses differential frame information from stabilized video sequences and the YOLOv7 classifier. A convolutional network classifier was used to detect the roofs of buildings, using a custom training set adapted to the type of data used. Such a solution, although quite effective, is not very practical for the end user, but it enables the automatic generation of a comprehensive training set for classifiers based on deep neural networks. The effectiveness of such a solution was tested for the latest version of the YOLOv7 classifier. The tests proved the effectiveness of the described method, both for single images and video sequences. In addition, the obtained classifier correctly recognizes objects for sequences that do not meet some of the initial assumptions, such as the angle of the camera capturing the image.https://www.mdpi.com/1424-8220/23/4/2235air pollutionaerial imagingimage processingobject detectiondeep learningvideo processing |
| spellingShingle | Marek Szczepański Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles Sensors air pollution aerial imaging image processing object detection deep learning video processing |
| title | Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles |
| title_full | Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles |
| title_fullStr | Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles |
| title_full_unstemmed | Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles |
| title_short | Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles |
| title_sort | vision based detection of low emission sources in suburban areas using unmanned aerial vehicles |
| topic | air pollution aerial imaging image processing object detection deep learning video processing |
| url | https://www.mdpi.com/1424-8220/23/4/2235 |
| work_keys_str_mv | AT marekszczepanski visionbaseddetectionoflowemissionsourcesinsuburbanareasusingunmannedaerialvehicles |