用于生产线精确质量检测的协作式视觉传感系统

Jiale Chen, Duc Van Le, Rui Tan, Daren Ho
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摘要

视觉传感已被广泛应用于生产过程中的质量检测。本文介绍了一种名为 BubCam 的智能协作摄像系统的设计与实施,该系统用于惠普(HP)公司工厂对生产的油墨袋进行自动质量检测。具体来说,BubCam 可估算油墨袋中可能影响印刷质量的气泡体积。由于动态环境光反射、运动模糊效应和数据标记困难,BubCam 的设计面临挑战。作为起点,我们设计了一个单摄像头系统,该系统利用了各种基于深度学习(DL)的图像分割和深度融合技术。我们提出了新的数据标注和训练方法,以利用生产系统的先验知识,通过小型数据集训练分割模型。然后,我们设计了一个多摄像头系统,该系统额外部署了多个无线摄像头,通过多视角传感实现更高的精度。为了节省无线摄像头的功耗,我们提出了一个配置适应问题,并开发了基于单代理和多代理深度强化学习(DRL)的解决方案,以根据气泡存在和光反射的变化调整每个无线摄像头的运行模式和帧速率。多代理 DRL 方法旨在减少生产线重新配置过程中的重新培训成本,只需针对新添加的摄像头和位置发生变化的现有摄像头重新培训 DRL 代理即可。在实验室测试平台和实际工厂试验中进行的广泛评估表明,BubCam 优于六种基准解决方案,包括当前的人工检测和现有的气泡检测与摄像头配置适应方法。与手动检测方法相比,BubCam 的准确性提高了 1.3 倍,延迟缩短了 300 倍。
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A Collaborative Visual Sensing System for Precise Quality Inspection at Manufacturing Lines
Visual sensing has been widely adopted for quality inspection in production processes. This paper presents the design and implementation of a smart collaborative camera system, called BubCam , for automated quality inspection of manufactured ink bags in Hewlett-Packard (HP) Inc.’s factories. Specifically, BubCam estimates the volume of air bubbles in an ink bag, which may affect the printing quality. The design of BubCam faces challenges due to the dynamic ambient light reflection, motion blur effect, and data labeling difficulty. As a starting point, we design a single-camera system which leverages various deep learning (DL)-based image segmentation and depth fusion techniques. New data labeling and training approaches are proposed to utilize prior knowledge of the production system for training the segmentation model with a small dataset. Then, we design a multi-camera system which additionally deploys multiple wireless cameras to achieve better accuracy due to multi-view sensing. To save power of the wireless cameras, we formulate a configuration adaptation problem and develop the single-agent and multi-agent deep reinforcement learning (DRL)-based solutions to adjust each wireless camera’s operation mode and frame rate in response to the changes of presence of air bubbles and light reflection. The multi-agent DRL approach aims to reduce the retraining costs during the production line reconfiguration process by only retraining the DRL agents for the newly added cameras and the existing cameras with changed positions. Extensive evaluation on a lab testbed and real factory trial shows that BubCam outperforms six baseline solutions including the current manual inspection and existing bubble detection and camera configuration adaptation approaches. In particular, BubCam achieves 1.3x accuracy improvement and 300x latency reduction, compared with the manual inspection approach.
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