Edge-Eye: Rectifying Millimeter-level Edge Deviation in Manufacturing using Camera-enabled IoT Edge Device

Irradiated Cross-linked Polyethylene Foam (IXPE) has been one of the most commonly used materials in industry. During the production process of IXPE sheets, their edges need keep aligned strictly, otherwise, they could quickly get out of the border of the rolling plate and cause the huge economic loss. In this paper, we propose a camera-enabled approach, called Edge-Eye, to rectify the edge deviation automatically for IXPE production with millimeter-level accuracy. We deploy a commercial camera with mobile edge node in front of the IXPE sheet to continuously detect and rectify the edge deviation. Particularly, to handle the complex production en-vironment when extracting the edge of IXPE sheet, we deploy a pair of reference bars with high-contrast colors to efficiently differ-entiate the sheet edge from the background. Then, we propose a Bi-direction Edge Tracking method to perform the edge detection from both vertical and horizontal aspects. To realize the rectification using mobile edge nodes with limited computing resources, we reduce the cost of computation by extracting the Minimized Region of Interest, i.e., the edge area overlapped with the higher contrast reference bar on both sides. We further design a negative feedback control system with multi-stage feedback regulation mechanism, keeping the edge deviation within millimeter-level. We implemented Edge-Eye on the ARM64 platform and performed evaluation in the practical IXPE production process. The experimental results show that Edge-Eye achieves the average accuracy of 5mm for the edge deviation rectification, with the average latency of 200ms for edge deviation detection. During the process of 20-month real deployment for 36 production lines, 66 manpower per day (90% of the overall manpower) has been saved, and the utilization rate of IXPE material increases from 87% to 94%.