摘要
随着我国经济的高速全面发展,人民生活水平的日益提高,我国对于交通运输方面的需求越来越大。为了满足列车安全运行的需求,本文提出铁路场景下基于单阶段实例分割的列车主动避障视觉算法,针对铁路场景中侵线情况下检测物体多重叠的特点对算法模型进行了优化,改进主干网络和多尺度融合方法提高了模型的精度,利用TensorRT半精度加速和CUDA重构对模型进行了加速,并对本文方法和其他方法进行性能评价与对比试验。最终,本文方法在嵌入式平台Xavier上实现了71.2MAP和108 ms的速度,实现了车载部署下列车前方环境的高效高精度检测。
With the rapid and comprehensive development of China′s economy and the improvement of people′s living standards, China’s demand for transportation is increasing. In order to meet the higher requirements of locomotive operation safety, a visual algorithm of the advanced driver assistance system of trains based on single-stage instance segmentation in railway scene is proposed. The algorithm model is optimized for detecting the characteristics of multiple overlaps of objects in railway scenes. The accuracy of the model is improved by improving the Backbone network and multi-scale fusion method. The model is accelerated by TensorRT semi-precision acceleration and CUDA code refactoring. The performance evaluation and comparative test of this method and other methods are carried out. Finally, this method achieves 71.2 MAP and 108 ms on the embedded platform Xavier. High-precision detection of the surrounding environment of the train under vehicle deployment is realized.
作者
姚巍巍
田野
李晨
Yao Weiwei;Tian Ye;Li Chen(Data and Intelligence R&D Center,Zhuzhou CRRC Times Electric Co.,Ltd.,Zhuzhou 412001,China)
出处
《电子测量技术》
北大核心
2022年第9期133-139,共7页
Electronic Measurement Technology
关键词
深度学习
实例分割
标签分配
模型加速
deep learning
instance segmentation
label assignment
model acceleration
