摘要
Abstract
In rotary-wing unmanned aerial vehicle(UAV)video streams,road scenes often suffer from object occlusion and low-contrast lighting interference,which can lead to segmentation ambiguity.Therefore,this study proposed a real-time road segmentation algorithm for rotary-wing UAV video streams based on mask region-based convolutional neural network(Mask R-CNN).For video streams captured by rotary-wing UAVs,considering the multi-scale geometric heterogeneity and environmental noise coupling interference of road features,the study first constructed a multi-scale feature pyramid enhance-ment module based on Mask R-CNN.Additionally,the noise distribution was simulated by generating multiplicative noise through a parameterized Gamma distribution.Then,a spatially adaptive attention window was introduced to dynamically adjust the feature map resolution based on pixel-level true positive rate.Road feature values were used as reference quantities for the fitting function,and a parameterized function model was constructed.Finally,using the actual geometric feature val-ues of the road mask as independent variables and the real-time spatial coordinates and topology of the road targets in the video stream as dependent variables,the mapping relationship was described between geometric properties and dynamic vari-ables in the video stream.Road point cloud feature similarity was calculated,and effective segmentation was achieved through point cloud mapping comparison.Experimental data shows that this method can effectively handle the effects of dark,complex,and high-exposure environmental conditions,with high accuracy in real-time road segmentation and a high signal-to-noise ratio in the segmented images.关键词
掩膜区域卷积神经网络/旋翼无人机/实时道路分割/道路掩码/伽马分布Key words
mask region-convolutional neural network(Mask R-CNN)/rotary-wing unmanned aerial vehicle(UAV)/real-time road segmentation/road mask/gamma distribution分类
计算机与自动化
