Abstract
Acceptance sampling is an essential procedure in the statistical quality control domain to inspect incoming lots before shipping or producing vital products, including medicine, electronic components, automobile parts, and processed food. Prior research has shown that a visual surface inspection of incoming lots is typically done by experienced inspectors who find between 70% to 80% of the defective parts and spend on average 54 seconds on each part. The low speed and low accuracy of inspection tangibly decrease the quality of products and impairs the economic benefits of enterprises. We propose a novel CNN architecture named InspectNet designed for deployment to edge devices. The architecture focuses on maximizing the accuracy and minimizing the inference time of detection by coalescing two state-of-the-art CNN architectures. InspectNet yielded an accuracy of 98.5% and a detection speed of 0.12 seconds which is competitive to state-of-the-art models. The second phase of the dissertation focuses on deploying InspectNet to a novel portable multi-camera inspection system named Qbox and the potential applications. The Qbox generalizability, capabilities, and potentials were demonstrated through a case study where plastic elbow adapters were successfully and efficiently trained on InspectNet using transfer learning and then inspected for surface anomalies using our proposed system.