基于改进YOLOv5s模型的地铁屏蔽门与列车门间异物快速检测方法

A Method for Timely Detecting Foreign Objects between Metro Platform Screen Doors and Train Doors Based on an Improved YOLOv5s Model

快速准确地检测地铁屏蔽门与列车门间异物对于保障安全具有重要意义。针对当前地铁屏蔽门与列车门间异物检测方法的低效和不准确,提出了1种基于YOLOv5s模型的快速检测方法。由于原始YOLOv5s模型在检测异物时仅依赖于候选区域内部特征信息而忽略了全局语义信息,因此引入全局语义模块来解决这一局限。该模块集成了非局部模块和压缩-激励模块:非局部模块采用自注意力机制建模像素对关系,捕获长局信息依赖;压缩-激励模块则起到降低模型计算量的作用。全局语义模块使得模型能够捕获全局语义信息并将其与局部信息相结合,以实现更好的异物检测,同时不会显著增加计算复杂度。此外,原始YOLOv5s模型中低效的Focus模块被1个完全由标准卷积单元构成的Stem模块所取代,有助于减少模型计算量和提高检测速度。使用桌面级显卡NVIDIA TITAN Xp,在从真实地铁站中采集构建而成的5854张地铁异物数据集,对模型进行验证,实验结果表明:(1)改进后的YOLO模型表现显著优于其它基准模型,检测速度达到385帧/s,相比原始YOLOv5s提升100%,相比最快的YOLOv3-SPP提升466%;(2)改进后的YOLO模型实现了88.5%的检测平均准确率,相比原始YOLOv5s提升0.5%,相比检测平均准确率最高的YOLOv3-SPP提升0.6%;(3)此外,改进后的YOLO模型仅占用空间14.4 MB的计算机存储空间,相比原始YOLOv5s减少0.7%,相比所占空间最小的SSD减少85%。

Accurately and efficiently detecting foreign objects between platform screen doors(PSDs)and train doors at metro stations is of great significance for safety purpose.In response to the inefficiency and inaccuracy of current detection methods,a method based on the you-only-look-once(YOLOv5s)model is proposed.As the original YOLOv5s model relies on internal features of candidate regions but not global contextual information,a global context module is introduced to address the limitation.This module integrates non-local modules and squeeze-excitation modules.The non-local modules use self-attention mechanism to model relationships between pixels and capture long-term dependencies.The squeeze-excitation modules is developed to reduce the computational cost of the model.The global context module enables the model to capture global contextual information and combines it with local information for improved detection of foreign objects without significantly increasing computational complexity.Additionally,the inefficient Focus module of the original YOLOv5s is replaced with a Stem module that is fully developed from standard convolutional units,contributing to a reduced computation cost and enhanced detection speed.Experiments are conducted based on a dataset of 5854 foreign object images collected from metro stations,with the model being tested using desktop-level NVIDIA TITAN Xp graphics cards.The results indicate that①the improved YOLO model performs remarkably better than other baseline models,exhibiting an impressive detection speed of 385 frames per second,a 100% improvement over the original YOLOv5s model and a substantial 466% improvement over the fastest speed of YOLOv3-SPP model.②The improved YOLO model achieves an average detection accuracy of 88.5%,a 0.5% improvement over the original YOLOv5s and a 0.6% improvement over the highest average detection accuracy of YOLOv3-SPP.③The improved YOLO model takes up only 14.4 MB of computer storage space,which is 0.7% less than the original YOLOv5s,and 85% less than the single shot multibox detector(SSD)that takes the least storage space.