| Abstrakt: |
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). [ABSTRACT FROM AUTHOR] |
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