用YOLOv5实时检测铁轨及附近1米内的人，防事故
Detection of Rail Line Track and Human Beings Near the Track to Avoid Accidents
- 基于YOLOv5的实时视频分析，精准定位铁轨与近轨人体
- 在1米范围内识别移动物体，准确率显著优于现有方法
- 适合铁路安全监控系统，尤其适用于高风险区域
本文提出一种利用YOLOv5深度学习模型进行铁路轨道检测及轨道附近人体识别的方法，以降低潜在事故风险。该技术通过实时视频数据，高精度识别铁路轨道，并在距轨道1米范围内检测到移动物体，重点针对人类目标。系统可实时发出警报，警示轨道旁有人存在，提升铁路环境安全性。此外，还具备远距离物体识别功能，进一步增强预防能力。通过全面评估验证，该方法在准确性上相比现有技术有显著提升，展现出在铁路安全领域实现变革的巨大潜力，为事故预防策略提供重要支持。
原文摘要 · Abstract (English)
This paper presents an approach for rail line detection and the identification of human beings in proximity to the track, utilizing the YOLOv5 deep learning model to mitigate potential accidents. The technique incorporates real-time video data to identify railway tracks with impressive accuracy and recognizes nearby moving objects within a one-meter range, specifically targeting the identification of humans. This system aims to enhance safety measures in railway environments by providing real-time alerts for any detected human presence close to the track. The integration of a functionality to identify objects at a longer distance further fortifies the preventative capabilities of the system. With a precise focus on real-time object detection, this method is poised to deliver significant contributions to the existing technologies in railway safety. The effectiveness of the proposed method is demonstrated through a comprehensive evaluation, yielding a remarkable improvement in accuracy over existing methods. These results underscore the potential of this approach to revolutionize safety measures in railway environments, providing a substantial contribution to accident prevention strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。