机器视觉通常用于分析图像,并生成一个对被生成图像物体或场景的描述,这些描述最终用于辅助或决定机器人控制决策。 一门基于计算机图像识别和分析的技术。主要用于自动检测,流程控制或机器人引导等。

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摘要:城市轨道交通系统主要由弓/网系统、轨道线路、车辆、车站等组成, 传统的人工巡检等方法检测效率低、劳动强度大、自动化和智能化程度不高, 给城市轨道交通的运营保障和进一步健康发展带来了巨大的挑战.机器视觉作为一种重要的检测手段, 在城市轨道交通系统状态检测领域得到了广泛的应用.鉴于此, 针对机器视觉在城市轨道交通系统安全状态检测中的研究和应用进行综述.首先, 简要介绍城市轨道交通的基本概念和快速发展所面临的挑战与机遇.然后, 详细介绍机器视觉技术在城市轨道交通各子系统安全状态检测中的研究与应用情况; 针对弓/网系统状态检测问题, 分别重点介绍机器视觉在受电弓磨耗检测、受电弓包络线等其他病害检测、接触网几何参数检测、接触网磨耗检测以及接触网悬挂病害检测中的国内外研究现状; 在轨道线路安全状态检测方面, 分别介绍机器视觉在扣件安全状态检测和钢轨表面病害检测中的应用与研究现状; 从不同检测项点角度详细介绍机器视觉在车辆状态检测中的应用与研究进展; 梳理和总结机器视觉在车站电扶梯安全监控和站台安全监控的异常行为检测中的具体应用和研究; 并重点介绍机器视觉在轨道交通司机行为监测中的具体应用和背景技术.最后, 对机器视觉技术应用于城市轨道交通系统状态检测领域的未来进行展望.

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Because manufacturing processes evolve fast, and since production visual aspect can vary significantly on a daily basis, the ability to rapidly update machine vision based inspection systems is paramount. Unfortunately, supervised learning of convolutional neural networks requires a significant amount of annotated images for being able to learn effectively from new data. Acknowledging the abundance of continuously generated images coming from the production line and the cost of their annotation, we demonstrate it is possible to prioritize and accelerate the annotation process. In this work, we develop a methodology for learning actively, from rapidly mined, weakly (i.e. partially) annotated data, enabling a fast, direct feedback from the operators on the production line and tackling a big machine vision weakness: false positives. We also consider the problem of covariate shift, which arises inevitably due to changing conditions during data acquisition. In that regard, we show domain-adversarial training to be an efficient way to address this issue.

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Because manufacturing processes evolve fast, and since production visual aspect can vary significantly on a daily basis, the ability to rapidly update machine vision based inspection systems is paramount. Unfortunately, supervised learning of convolutional neural networks requires a significant amount of annotated images for being able to learn effectively from new data. Acknowledging the abundance of continuously generated images coming from the production line and the cost of their annotation, we demonstrate it is possible to prioritize and accelerate the annotation process. In this work, we develop a methodology for learning actively, from rapidly mined, weakly (i.e. partially) annotated data, enabling a fast, direct feedback from the operators on the production line and tackling a big machine vision weakness: false positives. We also consider the problem of covariate shift, which arises inevitably due to changing conditions during data acquisition. In that regard, we show domain-adversarial training to be an efficient way to address this issue.

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