Abstract
In this paper, we consider object classification and detection problems for autonomous UAVs. 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 an autonomous UAV control system. 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. The PSN-10 inflatable life raft (an example of an object that is detected during rescue operations using UAVs) and oil tank storage (such kind of objects are usually detected during the inspection of industrial infrastructure) are considered as target objects in this work. The performance of the trained detectors was estimated on real data (including data obtained during the real rescue operation of the trawler “Dalniy Vostok” in 2015).
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This work is partially supported by the Russian Foundation for Basic Research (projects 18–29-26022 and 18–29-2602).
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Usilin, S.A., Slavin, O.A., Arlazarov, V.V. (2021). Memory Consumption and Computation Efficiency Improvements of Viola-Jones Object Detection Method for UAVs. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_23
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