WIRELESS UAV CHARGING SYSTEM WITH BATTERY BALANCING FUNCTIONALITY

The main volume of requirements in early detection systems are imposed on the performance of digitalimage processing algorithms that are implemented on embedded devices with limited computing resources.In the early detection problem, objects in images are represented by a small number of pixels.Therefore, in order to ensure the required accuracy characteristics of the algorithms for searching andrecognizing objects in images, algorithms for preliminary processing of a sequence of video frames areused to expand the original feature space. Processing high-resolution images by image preliminary processingalgorithms leads to an unacceptable time delay in the execution of the algorithm and is a "bottleneck"of the entire algorithm. In this paper, an algorithm for preliminary processing of a sequence of videoframes for a neural accelerator is proposed in order to expand the feature space, which allows increasingthe speed of data processing. This is achieved by merging the image preliminary processing algorithmwith the feature extractor of a convolutional neural network and transferring the execution of a new featureextractor to the computing power of the neural accelerator. The developed algorithm was tested byconducting a computational experiment. On NVIDIA Jetson and Rockchip computing devices, the algorithmof preliminary processing is implemented twice on the central processor and neural accelerator,according to the developed algorithm. Estimates of the execution time of the algorithms are obtained,which show that the proposed algorithm of preliminary image processing for the neural accelerator allows increasing the data processing speed by 1.4 - 4 times depending on the type of bit depth of calculations.However, the transition to the integer type of calculations of the CNN model with a modified featureextractor leads to a decrease in the Mean Average Precision metric by 5–19.4%, characterizing the integralaverage accuracy of searching and recognizing objects in images