Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers

Ruslan Abdulkadirov, Pavel Lyakhov (), Denis Butusov (), Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov and Diana Kalita
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Ruslan Abdulkadirov: Department of Mathematical Modelling, North-Caucasus Federal University, 355009 Stavropol, Russia
Pavel Lyakhov: Department of Mathematical Modelling, North-Caucasus Federal University, 355009 Stavropol, Russia
Denis Butusov: Computer-Aided Design Department, St. Petersburg Electrotechnical University “LETI”, 5 Professora Popova St., 197022 Saint Petersburg, Russia
Nikolay Nagornov: Department of Mathematical Modelling, North-Caucasus Federal University, 355009 Stavropol, Russia
Dmitry Reznikov: Department of Mathematical Modelling, North-Caucasus Federal University, 355009 Stavropol, Russia
Anatoly Bobrov: Department of Mathematical Modelling, North-Caucasus Federal University, 355009 Stavropol, Russia
Diana Kalita: Department of Mathematical Modelling, North-Caucasus Federal University, 355009 Stavropol, Russia

Mathematics, 2025, vol. 13, issue 5, 1-22

Abstract: The current development of machine learning has advanced many fields in applied sciences and industry, including remote sensing. In this area, deep neural networks are used to solve routine object detection problems, satisfying the required rules and conditions. However, the growing number and difficulty of such problems cause the developers to construct machine learning models with higher computational complexities, such as an increased number of hidden layers, epochs, learning rate, and rate decay. In this paper, we propose the Yolov8 architecture with decomposed layers via canonical polyadic and Tucker methods for accelerating the solving of the object detection problem in satellite images. Our positive–negative momentum approaches enabled a reduction in the loss in precision and recall assessments for the proposed neural network. The convolutional layer factorization reduces the shapes and accelerates the computations at kernel nodes in the proposed deep learning models. The advanced optimization algorithms achieve the global minimum of loss functions, which makes the precision and recall metrics superior to the ones for their known counterparts. We examined the proposed Yolov8 with decomposed layers, comparing it with the conventional Yolov8 on the DIOR and VisDrone 2020 datasets containing the UAV images. We verified the performance of the proposed and known neural networks on different optimizers. It is shown that the proposed neural network accelerates the solving object detection problem by 44–52%. The proposed Yolov8 with Tucker and canonical polyadic decompositions has greater precision and recall metrics than the usual Yolov8 with known analogs by 0.84–0.94 and 0.228–1.107 percentage points, respectively.

Keywords: optimization; positive–negative moments; tensor decomposition; remote sensing; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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