20.500.12556/RUL-171574 SqueezeSlimU-Net an adaptive and efficient segmentation architecture for real-time UAV weed detection The limited processing capacity of computing equipment that is usually mounted on unmanned aerial vehicles (UAVs) often prevents real-time execution of computer vision tasks, such as image segmentation. In this article, we introduce SqueezeSlimU-Net (SSU-Net), an adaptive and efficient deep learning (DL) model designed to enhance UAV capabilities in performing complex image segmentation tasks under resource constraints, thereby advancing real-time UAV vision—a crucial technology in fields, such as precision agriculture. SSU-Net combines benefits of three specialized DL architectures: the semantic segmentation capabilities of the U-Net architecture, the computational efficiency of SqueezeNet's fire modules, and the dynamic adaptability of slimmable neural networks. This integration allows SSU-Net to adjust its network width in real-time, thus striking the balance between inference accuracy and computational load based on the operational parameters such as task requirements and UAV's battery life. To validate SSU-Net's efficacy, we applied it to a weed detection task using two UAV-collected datasets and tested it on an edge computing platform for UAVs. Our experiments show that SSU-Net can reduce inference energy consumption by up to 65% with only a minimal 2% reduction in accuracy. A comparative evaluation with other state-of-the-art DL image segmentation approaches shows that SSU-Net achieves on par weed detection performance while requiring significantly fewer model parameters. In addition, SSU-Net outperforms state-of-the-art network pruning techniques in balancing accuracy and resource usage. Timing benchmarks show SSU-Net fostering real-time weed detection even on low-resource UAVs, making it ideal for UAV remote sensing applications. adaptive neural networks computational effciency image segmentation precision agriculture real-time unmanned aerial vehicle vision UAV weed detection prilagodljive nevronske mreže računalniška učinkovitost segmentacija slike natančno poljedelstvo vid brezpilotnega letala v realnem času odkrivanje plevela true false true Angleški jezik Slovenski jezik Članek v reviji 2025-08-28 09:52:47 2025-08-28 09:52:49 2025-09-07 04:06:08 0000-00-00 00:00:00 2025 0 0 Str. 5749-5764 Vol. 18 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.93:632.51 1939-1404 10.1109/JSTARS.2025.3536175 228027395 RAZ_Machidon_Alina_Luminita_2025.pdf RAZ_Machidon_Alina_Luminita_2025.pdf 1 AC977D33BEDC79E0724C8E9A12E2936E 23b5a8ba91356c7f890ad2fe5a86bddf607de5328d801668bd738b3ac890e8ed 2a2e3510-83ec-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=214950 https://ieeexplore.ieee.org/abstract/document/10857312 1 de095aca-83e3-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=214938 Fakulteta za računalništvo in informatiko 0 0 0