控制与信息技术Issue(4)：90-95,6.DOI:10.13889/j.issn.2096-5427.2024.04.012
基于神经网络架构搜索的铁道障碍物目标检测模型优化研究
Research on Optimization of Railway Obstacle Detection Model Based on Neural Architecture Search
摘要
Abstract
The automatic perception of train operating environments leveraging neural networks has emerged as a new approach critical for ensuring the safe operation of trains. However,traditional neural network models primarily rely on a trial-and-error process conducted by human experts,along with accumulated experience,which is not only time-consuming and tedious,but also hardly guarantees optimum performance for models. To address this issue,this paper proposes a method for optimizing railway obstacle detection models based on zero-cost neural architecture search. This method begins with the construction of an comprehensive space of potential model architectures. On this basis,the search scope is effectively constrained according to computational workloads required in real-world applications,ensuring that the selected models meet requirements both in accuracy and operational efficiency. The subsequent utilization of a zero-cost neural architecture search algorithm allows for quickly predicting the practical performance of various architectures,without the need for tedious and time-consuming actual training. Furthermore,a comparison of expected performance scores across different models leads to the selection of the optimal option as the ultimate solution. Experimental results demonstrated that this method achieved an average accuracy of 0.711 and an average inference time of 6.12 ms per frame,significantly outperforming baseline models with equivalent computational loads.关键词
铁道障碍物/深度学习/自动感知/目标检测/神经网络架构搜索Key words
railway obstacle/deep learning/automatic perception/object detection/neural architecture search分类
信息技术与安全科学引用本文复制引用
姚巍巍,吕宇,张慧源,熊敏君,董文波,李晨..基于神经网络架构搜索的铁道障碍物目标检测模型优化研究[J].控制与信息技术,2024,(4):90-95,6.基金项目
国家重点研发计划项目(2022YFB4300602) （2022YFB4300602）
