Learning Geometric-Aware and Weather-Adaptive Semantics in Remote Sensing: Affine Lie Group Enhanced Detector for UAV Road Scenes

Jialang Liu, Jialei Zhan, Yanming Guo

Abstract: Reliable road condition detection using drone imagery is critically important, particularly under harsh weather conditions such as rain, fog, and snow, which cause reduced visibility and blurred objects. Traditional detection methods are limited in effectively handling these severe scenarios due to their static feature extraction approaches. To address these challenges, we propose an innovative affine lie group convolution and weather-adaptive feature enhancement network (ALGC-WFEMNet). The core innovation of this method lies in the affine lie group convolution (ALGC), which leverages the mathematical framework of affine lie groups to introduce a dynamic convolution mechanism. This mechanism adaptively modifies convolution kernels based on affine transformations, significantly enhancing the model’s robustness against weather-induced variations in scale, rotation, and visibility. Furthermore, the ALGC framework integrates a learning-based weather condition coefficient, dynamically adjusting kernel responses to specific environmental conditions such as rain, fog, and snow. This theoretical advancement not only emphasizes the mathematical novelty of applying affine lie groups in convolutional neural networks but also substantially improves feature extraction and adaptability for object detection tasks. Experimental validation on UAV-based road inspection datasets demonstrates that our ALGC-WFEMNet achieves a mean average precision (mAP) of 60.48%. Furthermore, we deploy the model within a UAV-IoT system to verify its practical effectiveness, achieving an inference time of 23.31 seconds on a Raspberry Pi.