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A Sensor Planning System For Automated Headlamp Lens Inspection

机译:用于自动前照灯镜片检查的传感器计划系统

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摘要

Usually, the automated systems for quality control based on computer vision have been centered on the design of algorithms for detecting different types of defects. Nevertheless, the issues related to planning suitable sensor poses for the inspection task have received less attention. In addition, the applications where a vision sensor can only sample a portion of a part from a single viewpoint, the sensor planning problem becomes critically important. This is the case of the automated inspection of vehicle headlamp lens, that due to its geometry and dimensions, requires multiple sensor poses to observe the whole part. Moreover, the customer requirements that define the maximum defect size should also be taken into account in the inspection process.rnThis paper presents a vision sensor planning system applied to the quality control of headlamp lenses. The system uses the lens CAD, a vision sensor model and the customer requirements, included through a fuzzy approach, to achieve an optimal set of viewpoints. To compute the number and distribution of the viewpoints, a genetic algorithm is used. Experimental results demonstrate the effectiveness of the sensor planning system on commercial lenses.
机译:通常,基于计算机视觉的用于质量控制的自动化系统已经集中在检测不同类型缺陷的算法设计上。然而,与为检查任务计划合适的传感器姿态有关的问题很少受到关注。此外,在视觉传感器只能从单个角度采样零件的一部分的应用中,传感器计划问题变得至关重要。车辆前照灯透镜的自动检查就是这种情况,由于其几何形状和尺寸,需要多个传感器姿势才能观察整个零件。此外,在检查过程中还应考虑定义最大缺陷尺寸的客户要求。本文提出了一种视觉传感器计划系统,该系统应用于前照灯镜片的质量控制。该系统使用镜头CAD,视觉传感器模型和客户要求(包括通过模糊方法获得的信息)来获得最佳视点集。为了计算视点的数量和分布,使用了遗传算法。实验结果证明了传感器计划系统在商业镜头上的有效性。

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