# Accelerated Rotation-Invariant Convolution for UAV Image Segmentation Source: [Accelerated Rotation-Invariant Convolution for UAV Image Segmentation]() Collection index: [llms.txt]() Data updated: 2025-12-17T05:03:46.185Z Authors: Manduhu Manduhu, Alexander Dow, Gerard Dooly, James Riordan Published: 2025-12-09T18:30:00.000Z ## Abstract Rotation invariance is essential for precise, object-level segmentation in UAV aerial imagery, where targets can have arbitrary orientations and exhibit fine-scale details. Conventional segmentation architectures like U-Net rely on convolution operators that are not rotation-invariant, leading to degraded segmentation accuracy across varying viewpoints. Rotation invariance can be achieved by expanding the filter bank across multiple orientations; however, this will significantly increase computational cost and memory traffic. In this paper, we introduce a GPU-optimized rotation-invariant convolution framework that eliminates the traditional data-lowering (im2col) step required for matrix-multiplication-based convolution. By exploiting structured data sharing among symmetrically rotated filters, our method achieves multi-orientation convolution with greatly reduced memory traffic and computational redundancy. We further generalize the approach to accelerate convolution with arbitrary (non-symmetric) rotation angles. Across extensive benchmarks, the proposed convolution achieves 20--55% faster training and 15--45% lower energy consumption than CUDNN, while maintaining accuracy comparable to state-of-the-art rotation-invariant methods. In the eight-orientation setting, our approach achieves up to 45% speedup and 41% energy savings on 256\\(\\times\\)256 inputs, and 32% speedup and 23% lower energy usage on 1024\\(\\times\\)1024 inputs. Integrated into a U-Net segmentation model, the framework yields up to 6% improvement in accuracy over the non-rotation-aware baseline. These results demonstrate that the proposed method provides an effective and highly efficient alternative to existing rotation-invariant CNN frameworks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on Semantic Drone Dataset (test)](): Semantic segmentation; Semantic Drone Dataset (test); Training Time (s): 4109.6. - [Plant Segmentation on Plant Data (test)](): Plant Segmentation; Plant Data (test); Test Accuracy: 78.64. - [Semantic Segmentation on Semantic Drone (test)](): Semantic segmentation; Semantic Drone (test); Test Accuracy: 93.91. - [Semantic Segmentation on Plant Data (test)](): Semantic segmentation; Plant Data (test); Accuracy: 75.61. ## Original sources and implementation [Original paper]() [PDF]()