A Simulated Dataset in Aerial Images using Simulink for Object Detection and Recognition
The understanding and implementation of object detection and classification algorithms help in deploying diverse applications of UAVs. There is a need for a simulated UAV dataset to incorporate a pipeline for various algorithms. To reduce human efforts, multiple simulators have been utilized to mimi...
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-06-01
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| Series: | International Journal of Cognitive Computing in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307422000146 |
| _version_ | 1797959172920180736 |
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| author | Payal Mittal Akashdeep Sharma Raman Singh |
| author_facet | Payal Mittal Akashdeep Sharma Raman Singh |
| author_sort | Payal Mittal |
| collection | DOAJ |
| description | The understanding and implementation of object detection and classification algorithms help in deploying diverse applications of UAVs. There is a need for a simulated UAV dataset to incorporate a pipeline for various algorithms. To reduce human efforts, multiple simulators have been utilized to mimic the real-time behavior of drones. Our work inspired simulators and can be considered by engineering students to create a dataset in a simulated environment. In this paper, we focused on the study of MATLAB-based Simulink through multiple environment settings. The core objective of the paper is to create a simulated dataset from the utilized quadcopter-based flight control model in MATLAB-based Simulink. In this customized model, few modifications have been made to obtain drone videos to detect object categories such as pedestrians, other drones and obstacles while navigating in a simulated environment. Additionally, these simulated images are annotated for aerial image interpretation with multiple object categories. The dataset is annotated and is freely downloadable from: https://bit.ly/38jlAsh. In this research study, we mainly focus on the process of drone simulation in the MATLAB-based Simulink model. Further, the captured dataset is verified on state-of-the-art object detectors such as YoloV3, TinyYolov3 etc. by evaluating the authenticity of the dataset. |
| first_indexed | 2024-04-11T00:29:43Z |
| format | Article |
| id | doaj.art-a41fcaccffbf4731bd823c335126adf1 |
| institution | Directory Open Access Journal |
| issn | 2666-3074 |
| language | English |
| last_indexed | 2024-04-11T00:29:43Z |
| publishDate | 2022-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj.art-a41fcaccffbf4731bd823c335126adf12023-01-08T04:15:02ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742022-06-013144151A Simulated Dataset in Aerial Images using Simulink for Object Detection and RecognitionPayal Mittal0Akashdeep Sharma1Raman Singh2University Institute of Engineering and Technology, Panjab University, Chandigarh, IndiaUniversity Institute of Engineering and Technology, Panjab University, Chandigarh, India; Corresponding Author: Telephone Number: +91-9814925790University of the West of Scotland, United KingdomThe understanding and implementation of object detection and classification algorithms help in deploying diverse applications of UAVs. There is a need for a simulated UAV dataset to incorporate a pipeline for various algorithms. To reduce human efforts, multiple simulators have been utilized to mimic the real-time behavior of drones. Our work inspired simulators and can be considered by engineering students to create a dataset in a simulated environment. In this paper, we focused on the study of MATLAB-based Simulink through multiple environment settings. The core objective of the paper is to create a simulated dataset from the utilized quadcopter-based flight control model in MATLAB-based Simulink. In this customized model, few modifications have been made to obtain drone videos to detect object categories such as pedestrians, other drones and obstacles while navigating in a simulated environment. Additionally, these simulated images are annotated for aerial image interpretation with multiple object categories. The dataset is annotated and is freely downloadable from: https://bit.ly/38jlAsh. In this research study, we mainly focus on the process of drone simulation in the MATLAB-based Simulink model. Further, the captured dataset is verified on state-of-the-art object detectors such as YoloV3, TinyYolov3 etc. by evaluating the authenticity of the dataset.http://www.sciencedirect.com/science/article/pii/S2666307422000146UAV SimulatorObject detectionSimulated datasetComputer visionDeep learning |
| spellingShingle | Payal Mittal Akashdeep Sharma Raman Singh A Simulated Dataset in Aerial Images using Simulink for Object Detection and Recognition International Journal of Cognitive Computing in Engineering UAV Simulator Object detection Simulated dataset Computer vision Deep learning |
| title | A Simulated Dataset in Aerial Images using Simulink for Object Detection and Recognition |
| title_full | A Simulated Dataset in Aerial Images using Simulink for Object Detection and Recognition |
| title_fullStr | A Simulated Dataset in Aerial Images using Simulink for Object Detection and Recognition |
| title_full_unstemmed | A Simulated Dataset in Aerial Images using Simulink for Object Detection and Recognition |
| title_short | A Simulated Dataset in Aerial Images using Simulink for Object Detection and Recognition |
| title_sort | simulated dataset in aerial images using simulink for object detection and recognition |
| topic | UAV Simulator Object detection Simulated dataset Computer vision Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666307422000146 |
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