A standalone and portable imaging detection system with embedded computing for automated defect inspection of microfluidic devices

Abstract

The existing methods for defect detection in PDMS microfluidic chips typically involve complex image recognition algorithms or manual inspection and still lack efficiency and reliability. Although some automatic defect detection methods have been proposed in recent years, most of them still rely on external computation systems to deploy. To address these challenges, we propose an independent portable defect detection system with embedded computing for microfluidic devices. This portable system is completely self-contained, integrating an image acquisition module, a control panel module, a power module, and an embedded computing control module to realize chip detection, processing, and result display functions. Experimental results show that the system can effectively detect most of the commonly seen defects in PDMS-based microfluidic chips, proving to be more efficient and reliable than manual inspection. With the control of the embedded system, two detection methods: template matching (based on comparison with standard samples) and automatic defect detection (based on surface defect recognition) were used to identify defects in PDMS-based microfluidic chips. The proposed system can automatically inspect and analyze chips without the need for external laboratory support and can provide a promising solution for future microfluidic chip manufacturing and operation.