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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The visual signal compression is a long-standing problem. Fueled by the recent advances of deep learning, exciting progress has been made. Despite better compression performance, existing end-to-end compression algorithms are still designed towards better signal quality in terms of rate-distortion optimization. In this paper, we show that the design and optimization of network architecture could be further improved for compression towards machine vision. We propose an inverted bottleneck structure for the encoder of the end-to-end compression towards machine vision, which specifically accounts for efficient representation of the semantic information. Moreover, we quest the capability of optimization by incorporating the analytics accuracy into the optimization process, and the optimality is further explored with generalized rate-accuracy optimization in an iterative manner. We use object detection as a showcase for end-to-end compression towards machine vision, and extensive experiments show that the proposed scheme achieves significant BD-rate savings in terms of analysis performance. Moreover, the promise of the scheme is also demonstrated with strong generalization capability towards other machine vision tasks, due to the enabling of signal-level reconstruction.

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