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Car Detection from Unmanned Aerial Vehicles Based on Deep Learning: A Comparative Study

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Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 618))

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Abstract

Aerial Images raise a key challenge for the object detection algorithm, because of the small scale of objects and interference from complex surfaces. To overcome this issue, this paper explores the achievement of these three cutting-edge convolution neural network (CNN) algorithms, namely a two-stage object detector, Mask R-CNN, which is the segmentation algorithm, and one-stage object detector YOLOv3 and YOLOv5. The key goal of this task is to undertake the most exhaustive comparison of these three models. We also conducted some experiments to find out the best model for object detection using different sizes of anchor scales. Various anchor scales are used so that the anchor value might not be the cause for poor performance on detection. For training and testing purposes, we used a large car dataset. The dataset was created using images of UAVs. In this study, YOLOv3 and YOLOv5 outperform Mask R-CNN in precision, and mAP. On the other hand, YOLOv5 outperforms YOLOv3 precision, f1-score, recall and mAP.

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Authors and Affiliations

  1. Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh

    Sohag Hossain, Sajjad Waheed & M. Abdullah

Authors
  1. Sohag Hossain
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  2. Sajjad Waheed
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  3. M. Abdullah
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Corresponding author

Correspondence to Sohag Hossain .

Editor information

Editors and Affiliations

  1. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  2. Mawlana Bhashani Science and Technology, Dhaka, Bangladesh

    Sajjad Waheed

  3. National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  4. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  5. Amity University, Jaipur, India

    Kanad Ray

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Hossain, S., Waheed, S., Abdullah, M. (2023). Car Detection from Unmanned Aerial Vehicles Based on Deep Learning: A Comparative Study. In: Kaiser, M.S., Waheed, S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-19-9483-8_6

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