A simulated dataset in aerial images using Simulink for object detection and recognition

Payal Mittal*, Akashdeep Sharma*, Raman Singh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Pages (from-to)144-151
Number of pages8
JournalInternational Journal of Cognitive Computing in Engineering
Early online date16 Jul 2022
Publication statusPublished - 22 Jul 2022


  • UAV Simulator
  • object detection
  • simulated dataset
  • computer vision
  • deep learning


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