BubCam: A Vision System for Automated Quality Inspection at Manufacturing Lines

Abstract

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 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 via multi-view sensing. To save power of the wireless cameras, we formulate a configuration adaptation problem and develop a deep reinforcement learning (DRL)-based solution to adjust each wireless camera's operation mode and frame rate in response to the changes of presence of air bubbles and light reflection. 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 1.34x accuracy improvement and 260x latency reduction, compared with the manual inspection approach.