Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers
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 diffi...
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| स्वरूप: | Статья |
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Multidisciplinary Digital Publishing Institute (MDPI)
2025
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ir-123456789-303912025-04-03T12:56:38Z Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers Abdulkadirov, R. I. Абдулкадиров, Р. И. Lyakhov, P. A. Ляхов, П. А. Nagornov, N. N. Нагорнов, Н. Н. Reznikov, D. K. Резников, Д. К. Bobrov, A. A. Бобров, А. А. Kalita, D. I. Калита, Д. И. Neural networks Tensor decomposition Optimization Positive–negative moments Remote sensing 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. 2025-04-03T12:54:23Z 2025-04-03T12:54:23Z 2025 Статья Abdulkadirov R., Lyakhov P., Butusov D., Nagornov N., Reznikov D., Bobrov A., Kalita D. Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers // Mathematics. - 2025. - 13 (5). - art. no. 828. - DOI: 10.3390/math13050828 https://dspace.ncfu.ru/handle/123456789/30391 en Mathematics application/pdf application/pdf Multidisciplinary Digital Publishing Institute (MDPI) |
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Репозиторий |
| language |
English |
| topic |
Neural networks Tensor decomposition Optimization Positive–negative moments Remote sensing |
| spellingShingle |
Neural networks Tensor decomposition Optimization Positive–negative moments Remote sensing Abdulkadirov, R. I. Абдулкадиров, Р. И. Lyakhov, P. A. Ляхов, П. А. Nagornov, N. N. Нагорнов, Н. Н. Reznikov, D. K. Резников, Д. К. Bobrov, A. A. Бобров, А. А. Kalita, D. I. Калита, Д. И. Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers |
| description |
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. |
| format |
Статья |
| author |
Abdulkadirov, R. I. Абдулкадиров, Р. И. Lyakhov, P. A. Ляхов, П. А. Nagornov, N. N. Нагорнов, Н. Н. Reznikov, D. K. Резников, Д. К. Bobrov, A. A. Бобров, А. А. Kalita, D. I. Калита, Д. И. |
| author_facet |
Abdulkadirov, R. I. Абдулкадиров, Р. И. Lyakhov, P. A. Ляхов, П. А. Nagornov, N. N. Нагорнов, Н. Н. Reznikov, D. K. Резников, Д. К. Bobrov, A. A. Бобров, А. А. Kalita, D. I. Калита, Д. И. |
| author_sort |
Abdulkadirov, R. I. |
| title |
Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers |
| title_short |
Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers |
| title_full |
Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers |
| title_fullStr |
Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers |
| title_full_unstemmed |
Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers |
| title_sort |
enhancing unmanned aerial vehicle object detection via tensor decompositions and positive–negative momentum optimizers |
| publisher |
Multidisciplinary Digital Publishing Institute (MDPI) |
| publishDate |
2025 |
| url |
https://dspace.ncfu.ru/handle/123456789/30391 |
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