research-article

A Real-Time Detection Drone Algorithm Based on Instance Semantic Segmentation

Authors:

Di ZhuAuthors Info & Claims

ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing

Pages 36 - 41

https://doi.org/10.1145/3376067.3376098

Published: 25 February 2020 Publication History

Abstract

With the rapid development of drones, drones are widely used in various fields and bring convenience to people's production and life. However, they also bring security problems to society and the country. Especially in airports or military areas, the flight of drones can cause some problems. In order to effectively supervise the drone, this paper proposes a real-time detection drone algorithm HR-YOLACT which is based on instance semantic segmentation, and designed a new drone data set. The proposed algorithm combines the real-time instance semantic segmentation algorithm YOLACT with the deep high-resolution representation classification network HRNet. Firstly, feature maps are extracted by HRNet's backbone network. Secondly, the feature pyramid network is used to further extract image features, so that the network has better classification ability. Finally, the improved prediction head is utilized to detect the boxes of drones. In addition, this paper uses cross entropy instead of focal loss as the loss function to obtain better network training speed and quality. The experimental results show that HR-YOLACT has faster detection speed and higher detection precision than existing popular real-time object detection and real-time instance semantic segmentation algorithms.

References

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Cited By

Noor ALi KTovar EZhang PWei B(2024)Fusion flow-enhanced graph pooling residual networks for Unmanned Aerial Vehicles surveillance in day and night dual visionsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108959136:PBOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108959

Index Terms

A Real-Time Detection Drone Algorithm Based on Instance Semantic Segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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cover image ACM Other conferences

ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing

December 2019

270 pages

ISBN:9781450376822

DOI:10.1145/3376067

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shanghai Jiao Tong University: Shanghai Jiao Tong University
Xidian University
TU: Tianjin University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 February 2020

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the Fundamental Research Funding for the Central Universities of Ministry of Education of China
the Special Project Funding for the Shanghai Municipal Commission of Economy and Information Civil-Military Inosculation Project ?Big Data Management System of UAVs?

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ICVIP 2019

ICVIP 2019: 2019 the 3rd International Conference on Video and Image Processing

December 20 - 23, 2019

Shanghai, China

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Cited By

Noor ALi KTovar EZhang PWei B(2024)Fusion flow-enhanced graph pooling residual networks for Unmanned Aerial Vehicles surveillance in day and night dual visionsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108959136:PBOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108959

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