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Memory Consumption and Computation Efficiency Improvements of Viola–Jones Object Detection Method for Remote Sensing Applications

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Abstract

In this paper, we consider object classification and detection problems. We propose an algorithm that is effective from the point of view of computational complexity and memory consumption. The proposed algorithm can be successfully used as a basic tool for building different remote sensing systems which are, in general, installed on UAVs. The algorithm is based on the Viola–Jones method. It is shown in the paper, that the Viola–Jones method is the most preferable approach to detect objects on-board UAVs, because it needs the least amount of memory and the number of computational operations to solve the object detection problem. To ensure sufficient accuracy, we use a modified feature: rectangular Haar-like features, calculated over the magnitude of the image gradient. To increase computational efficiency, the L1 norm was used to calculate the magnitude of the image gradient. To train orientation-independent complex classifier we use a more generic decision tree form of complex classifier instead of a cascade scheme. All mentioned improvements were evaluated during detection of the following objects: the PSN-10 inflatable life raft (an example of an object that is detected during rescue operations using UAVs), oil tank storage (such kind of objects are usually detected during the inspection of industrial infrastructure), and aircraft on an area of hardstand. The performance of the trained detectors was estimated on real data (including data obtained during the rescue operation of the trawler Dalniy Vostok and a subset of real images from the DOTA dataset).

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Funding

This work is partially supported by the Russian Foundation for Basic Research (project nos. 18-29-26022 and 18-29-26020).

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Building 2, 44, Vavilov str., 119333, Moscow, Russia

    S. A. Usilin, O. A. Slavin & V. V. Arlazarov

  2. Smart Engines Service LLC, 121205, Moscow, Russia

    S. A. Usilin, O. A. Slavin & V. V. Arlazarov

Authors
  1. S. A. Usilin
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  2. O. A. Slavin
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  3. V. V. Arlazarov
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Corresponding authors

Correspondence to S. A. Usilin, O. A. Slavin or V. V. Arlazarov.

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This manuscript is a completely original work of its authors; it has not been published before and will not be published in other sources.

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The content of the article does not give grounds for raising the issue of a conflict of interest.

Additional information

Sergey Alexandrovich Usilin. Born in 1986. Graduated from Moscow Institute of Physics and Technology in 2009. Obtained Candidate’s degree in 2018. Works as a Senior Researcher at the Federal Research Center Computer Science and Control, Russian Academy of Sciences. Scope of scientific interests: object detection, machine learning, recognition systems, and digital image processing.

Oleg Anatolevich Slavin. Born in 1963. Graduated from Moscow Institute of Radio Engineering, Electronics, and Automation in 1988. Obtained candidate’s and doctoral degrees in 2000 and 2011, respectively. Works as a Chief Research Officer and Head of Department no. 92 at the Federal Research Center Computer Science and Control, Russian Academy of Sciences. Scope of scientific interests: information systems and pattern recognition.

Vladimir Viktorovich Arlazarov. Born in 1976. Graduated from Moscow Institute of Steel and Alloys in 1999. Received his Candidate’s degrees in 2005. Works as a Head of Department no. 93 at the Federal Research Center Computer Science and Control, Russian Academy of Sciences. Scope of scientific interests: artificial intelligence, machine learning, recognition systems, and information technology.

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Usilin, S.A., Slavin, O.A. & Arlazarov, V.V. Memory Consumption and Computation Efficiency Improvements of Viola–Jones Object Detection Method for Remote Sensing Applications. Pattern Recognit. Image Anal. 31, 571–579 (2021). https://doi.org/10.1134/S1054661821030238

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