Applications and prospects of track obstacle detection techniques
As urbanization and transportation networks continue to expand,ensuring the detection and removal of obstacles on rail and metro tracks in time is critical to the safety of public transportation systems.The main goal of the research in this article is to provide an overview of current state-of-the-art track obstacle detection techniques,with a particular focus on deep learning-based applications,and to explore the latent future progress in this field.Deep learning techniques have been remarkably successful in the field of computer vision to effectively recognize and classify complex image data,and therefore have great potential for detecting obstacles on tracks.By analyzing these technological prospects in-depth,this article aims to clarify the future challenges and opportunities in the field of orbital obstacle detection,and to provide valuable insights and guidance for related research.Meanwhile,this article proposes a method based on dynamic background modeling and point clouds 3D-segmentation,intending to break through the limitations of mainstream methods.Through this integrated approach,it is hoped that rail obstacle detection systems can be improved,and that the safety of rail transportation can be increased,providing passengers with more reliable travel experiences.
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