摘要
针对铁路接触网异物类别多样、场景多变的开放目标检测问题,提出基于改进DINO目标检测算法的铁路接触网异物检测方法。首先,基于铁路接触网异物图像特征,利用EfficientNet网络替换原始模型中的Resnet主干网络,并结合卷积注意力模块(Convolutional Block Attention Module,CBAM)对EfficientNet网络进行改进,在颈部结构采用改进后的加权双向特征金字塔网络(Bidirectional Feature Pyramid Network,BiFPN),以增强模型对重要特征的关注度,提升检测性能;其次,通过马赛克数据增强和环境干扰等多种数据增强技术对输入图片数据进行处理,丰富样本数据的特征;最后,利用铁路人工智能平台实现铁路接触网异物检测的应用。结果表明:所提方法在性能方面表现优异,其平均精度均值可达89.87%,与YOLOv5,DETR和原始DINO这3种典型的目标检测算法相比,分别提高了6.40%,7.31%和5.75%。该方法能够满足行车线路上接触网异物的准确、快速及智能化识别要求,为接触网异物检测提供重要的技术支撑。
To tackle the complex issue of open-environment object detection in railway catenaries,characterized by diverse foreign object categories and varying operational scenarios,a foreign object detection method for railway catenaries based on an improved DINO model is proposed.Firstly,by leveraging the image characteristics of foreign object in railway catenaries,the EfficientNet network is employed to replace the ResNet backbone in the original model,further enhancing the Convolutional Block Attention Module(CBAM).Additionally,the neck structure incorporates the enhanced Weighted Bidirectional Feature Pyramid Network(BiFPN)to improve the model’s focus on critical features and enhance detection performance.Secondly,various data augmentation techniques,such as mosaic data augmentation and environmental disturbances,are employed to process the input image data,enriching the features of the sample data.Finally,the application of foreign object detection in railway catenaries is realized through a railway artificial intelligence platform.The results indicate that the proposed method excels in performance,achieving a mean Average Precision of 89.87%,outperforming YOLOv5,DETR and the original DINO by 6.40%,7.31%and 5.75%,respectively.This method meets the requirements for accurate,rapid,and intelligent identification of foreign objects on railway lines,offering vital technical support for the detection of foreign objects in catenaries.
作者
史天运
侯博
李国华
代明睿
SHI Tianyun;HOU Bo;LI Guohua;DAI Mingrui(China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)
出处
《中国铁道科学》
EI
CAS
CSCD
北大核心
2024年第4期158-167,共10页
China Railway Science
基金
国家自然科学基金资助项目(U2268217)。
关键词
铁路接触网
异物识别
目标检测
人工智能平台
卷积注意力模块
Railway contact network
Foreign object identification
Object detection
Artificial intelligence platform
Convolutional Block Attention Module(CBAM)
