Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology
Unmanned aerial vehicles (UAVs) also called as a drone comprises of a controller from the base station along with a communications system with the UAV. The UAV plane can be precisely controlled by a machine operator, similar to remotely directed aircraft, or with increasing grades of autonomy, as li...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/7111248 |
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| author | Nageswara Guptha M Y. K. Guruprasad Yuvaraja Teekaraman Ramya Kuppusamy Amruth Ramesh Thelkar |
| author_facet | Nageswara Guptha M Y. K. Guruprasad Yuvaraja Teekaraman Ramya Kuppusamy Amruth Ramesh Thelkar |
| author_sort | Nageswara Guptha M |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) also called as a drone comprises of a controller from the base station along with a communications system with the UAV. The UAV plane can be precisely controlled by a machine operator, similar to remotely directed aircraft, or with increasing grades of autonomy, as like autopilot assistance, up to completely self-directed aircraft that require no human input. Obstacle detection and avoidance is important for UAVs, particularly lightweight micro aerial vehicles, but it is a difficult problem to solve because pay load restrictions limit the number of sensors that can be mounted onto the vehicle. Lidar uses Laser for finding the distance between objects and vehicle. The speed and direction of the moving objects are detected and tracked with the help of radar. When many sensors are deployed, both thermal and electro-optro cameras have great clustering capabilities as well as accurate localization and ranging. The purpose of the proposed architecture is to create a fusion system that is cost-effective, lightweight, modular, and robust as well. Also, for tiny object detection, we recommend a novel Perceptual Generative Adversarial Network method that bridges the representation gap between small and large objects. It employs the Generative Adversarial Networks (GAN) algorithm, which iimproves object detection accuracy above benchmark models at the same time maintaining real-time efficiency in an embedded computer for UAVs. Its generator, in particular, learns to turn unsatisfactory tiny object representations into super-resolved items that are similar to large objects to deceive a rival discriminator. At the same time, its discriminator contests with the generator to classify the engendered representation, imposing a perceptual restriction on the generator: created representations of tiny objects must be helpful for detection. With three different obstacles, we were able to successfully identify and determine the magnitude of the barriers in the first trial. The accuracy of proposed models is 83.65% and recall is 81% which is higher than the existing models. |
| format | Article |
| id | doaj-art-6aecac6fcebd434aa3fe792ca4164e7e |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-6aecac6fcebd434aa3fe792ca4164e7e2025-02-03T01:32:06ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/7111248Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion TechnologyNageswara Guptha M0Y. K. Guruprasad1Yuvaraja Teekaraman2Ramya Kuppusamy3Amruth Ramesh Thelkar4Department of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Electronic and Electrical EngineeringDepartment of Electrical and Electronics EngineeringFaculty of Electrical and Computer EngineeringUnmanned aerial vehicles (UAVs) also called as a drone comprises of a controller from the base station along with a communications system with the UAV. The UAV plane can be precisely controlled by a machine operator, similar to remotely directed aircraft, or with increasing grades of autonomy, as like autopilot assistance, up to completely self-directed aircraft that require no human input. Obstacle detection and avoidance is important for UAVs, particularly lightweight micro aerial vehicles, but it is a difficult problem to solve because pay load restrictions limit the number of sensors that can be mounted onto the vehicle. Lidar uses Laser for finding the distance between objects and vehicle. The speed and direction of the moving objects are detected and tracked with the help of radar. When many sensors are deployed, both thermal and electro-optro cameras have great clustering capabilities as well as accurate localization and ranging. The purpose of the proposed architecture is to create a fusion system that is cost-effective, lightweight, modular, and robust as well. Also, for tiny object detection, we recommend a novel Perceptual Generative Adversarial Network method that bridges the representation gap between small and large objects. It employs the Generative Adversarial Networks (GAN) algorithm, which iimproves object detection accuracy above benchmark models at the same time maintaining real-time efficiency in an embedded computer for UAVs. Its generator, in particular, learns to turn unsatisfactory tiny object representations into super-resolved items that are similar to large objects to deceive a rival discriminator. At the same time, its discriminator contests with the generator to classify the engendered representation, imposing a perceptual restriction on the generator: created representations of tiny objects must be helpful for detection. With three different obstacles, we were able to successfully identify and determine the magnitude of the barriers in the first trial. The accuracy of proposed models is 83.65% and recall is 81% which is higher than the existing models.http://dx.doi.org/10.1155/2022/7111248 |
| spellingShingle | Nageswara Guptha M Y. K. Guruprasad Yuvaraja Teekaraman Ramya Kuppusamy Amruth Ramesh Thelkar Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology Journal of Advanced Transportation |
| title | Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology |
| title_full | Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology |
| title_fullStr | Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology |
| title_full_unstemmed | Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology |
| title_short | Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology |
| title_sort | generative adversarial networks for unmanned aerial vehicle object detection with fusion technology |
| url | http://dx.doi.org/10.1155/2022/7111248 |
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