控制与信息技术Issue(1)：65-72,8.DOI:10.13889/j.issn.2096-5427.2024.01.300
基于脉冲数据重构的高速铁路关键零部件检测算法
Detection Algorithm for Key Components on High-speed Railways:Reconstruction Based on Spike Data
摘要
Abstract
The accurate and fast localization of key components on high-speed railways in operation serves is one of the foundations for fault diagnosis of high-speed railways.However,utilizing ordinary industrial cameras for data collection on operational high-speed railways often results in motion blur or misses key components.On the other hand,training object detection models with data collected by ultra-high-speed spike cameras encounters challenges,such as difficulties in data annotation and model training.To this end,this paper proposes a detection method for key components on high-speed railways that leverages reconstruction based on spike data.Firstly,based on the principle of spike triggering,the light intensity was restored at different points following the inter-spike interval.Then,local adjustments to light intensity were made through global light intensity calculations.The adjusted results were further filtered and locally enhanced to reconstruct high-resolution grayscale images.Subsequently,after data cleaning and annotation based on the reconstructed images,the annotated data were used to train the detection model for key components on high-speed railways.Finally,the trained model was accelerated using TensorRT.Experimental results showed that the accelerated model achieved an accuracy of 98.7%and a single-frame inference rate of 3.5 ms both on average.The study findings lay a foundation for the engineering applications of the proposed algorithm.关键词
高速铁路/关键零部件/目标检测/脉冲视觉/图像重构/TensorRT加速Key words
high-speed railway/key component/object detection/spike vision/image reconstruction/TensorRT acceleration分类
信息技术与安全科学引用本文复制引用
董文波,李晨,熊敏君,田野,肖雄,姚巍巍..基于脉冲数据重构的高速铁路关键零部件检测算法[J].控制与信息技术,2024,(1):65-72,8.基金项目
科技创新2030—新一代人工智能重大项目(2021ZD0109800) （2021ZD0109800）
