An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model
During the recent decade, emerging technological and dramatic uses for drones were devised and accomplished, including rescue operations, monitoring, vehicle tracking, forest fire monitoring, and environmental monitoring, among others. Wildfires are one of the most significant environmental threats...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/4162007 |
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| author | Mohamad Reda A. Refaai Dhruva R. Rinku I. Thamarai null S. Meera Naresh Kumar Sripada Simon Yishak |
| author_facet | Mohamad Reda A. Refaai Dhruva R. Rinku I. Thamarai null S. Meera Naresh Kumar Sripada Simon Yishak |
| author_sort | Mohamad Reda A. Refaai |
| collection | DOAJ |
| description | During the recent decade, emerging technological and dramatic uses for drones were devised and accomplished, including rescue operations, monitoring, vehicle tracking, forest fire monitoring, and environmental monitoring, among others. Wildfires are one of the most significant environmental threats to wild areas and forest management. Traditional firefighting methods, which rely on ground operation inspections, have major limits and may threaten firefighters’ lives. As a result, remote sensing techniques, particularly UAV-based remotely sensed techniques, are currently among the most sought-after wildfire-fighting approaches. Current improvements in drone technology have resulted in significant breakthroughs that allow drones to perform a wide range of more sophisticated jobs. Rescue operations and forest monitoring, for example, demand a large security camera, making the drone a perfect tool for executing intricate responsibilities. Meanwhile, growing movement of the deep learning techniques in computer vision offers an interesting perspective into the project’s objective. They were used to identify forest fires in their beginning stages before they become out of control. This research describes a methodology for recognizing the presence of humans in a forest setting utilizing a deep learning framework and a human object detection method. The goal of identifying human presence in forestry areas is to prevent illicit forestry operations like illegal access into forbidden areas and illegal logging. In recent years, a lot of interest in automated wildfire identification utilizes UAV-based visual information and various deep learning techniques. This study focused on detecting wildfires at the beginning stages in forest and wilderness areas, utilizing deep learning-based computer vision algorithms that control and then mitigate massive damages to human life and forest management. |
| format | Article |
| id | doaj-art-ab8d1d71e7964b549afb2eb7ad999850 |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-ab8d1d71e7964b549afb2eb7ad9998502025-02-03T05:50:35ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/4162007An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning ModelMohamad Reda A. Refaai0Dhruva R. Rinku1I. Thamarai2null S. Meera3Naresh Kumar Sripada4Simon Yishak5Department of Mechanical EngineeringDepartment of Electronics and Communication EngineeringDepartment of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Computer Science and Artificial IntelligenceCollege of Engineering and Argo-Industrial TechnologyDuring the recent decade, emerging technological and dramatic uses for drones were devised and accomplished, including rescue operations, monitoring, vehicle tracking, forest fire monitoring, and environmental monitoring, among others. Wildfires are one of the most significant environmental threats to wild areas and forest management. Traditional firefighting methods, which rely on ground operation inspections, have major limits and may threaten firefighters’ lives. As a result, remote sensing techniques, particularly UAV-based remotely sensed techniques, are currently among the most sought-after wildfire-fighting approaches. Current improvements in drone technology have resulted in significant breakthroughs that allow drones to perform a wide range of more sophisticated jobs. Rescue operations and forest monitoring, for example, demand a large security camera, making the drone a perfect tool for executing intricate responsibilities. Meanwhile, growing movement of the deep learning techniques in computer vision offers an interesting perspective into the project’s objective. They were used to identify forest fires in their beginning stages before they become out of control. This research describes a methodology for recognizing the presence of humans in a forest setting utilizing a deep learning framework and a human object detection method. The goal of identifying human presence in forestry areas is to prevent illicit forestry operations like illegal access into forbidden areas and illegal logging. In recent years, a lot of interest in automated wildfire identification utilizes UAV-based visual information and various deep learning techniques. This study focused on detecting wildfires at the beginning stages in forest and wilderness areas, utilizing deep learning-based computer vision algorithms that control and then mitigate massive damages to human life and forest management.http://dx.doi.org/10.1155/2022/4162007 |
| spellingShingle | Mohamad Reda A. Refaai Dhruva R. Rinku I. Thamarai null S. Meera Naresh Kumar Sripada Simon Yishak An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model Advances in Materials Science and Engineering |
| title | An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model |
| title_full | An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model |
| title_fullStr | An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model |
| title_full_unstemmed | An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model |
| title_short | An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model |
| title_sort | enhanced drone technology for detecting the human object in the dense areas using a deep learning model |
| url | http://dx.doi.org/10.1155/2022/4162007 |
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