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Efficient Object Detection Model for Real-time UAV Application

Author

Listed:
  • Subrahmanyam Vaddi
  • Dongyoun Kim
  • Chandan Kumar
  • Shafqat Shad
  • Ali Jannesari

Abstract

Unmanned Aerial Vehicles (UAVs) equipped with vision capabilities have become popular in recent years. Many applications have especially been employed object detection techniques extracted from the information captured by an onboard camera. However, object detection on UAVs requires high performance, which has a negative effect on the result. In this article, we propose a deep feature pyramid architecture with a modified focal loss function, which enables it to reduce the class imbalance. Moreover, the proposed method employed an end to end object detection model running on the UAV platform for real-time application. To evaluate the proposed architecture, we combined our model with Resnet and MobileNet as a backend network, and we compared it with RetinaNet and HAL-RetinaNet. Our model produced a performance of 30.6 mAP with an inference time of 14 fps. This result shows that our proposed model outperformed RetinaNet by 6.2 mAP.

Suggested Citation

  • Subrahmanyam Vaddi & Dongyoun Kim & Chandan Kumar & Shafqat Shad & Ali Jannesari, 2021. "Efficient Object Detection Model for Real-time UAV Application," Computer and Information Science, Canadian Center of Science and Education, vol. 14(1), pages 1-45, February.
    • Handle: RePEc:ibn:cisjnl:v:14:y:2021:i:1:p:45
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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