Light guide plates are essential optical components widely used in a diverse range of applications ranging from medical lighting fixtures to back-lit TV displays. In this work, we introduce a fully-integrated, high-throughput, high-performance deep learning-driven workflow for light guide plate surface visual quality inspection (VQI) tailored for real-world manufacturing environments. To enable automated VQI on the edge computing within the fully-integrated VQI system, a highly compact deep anti-aliased attention condenser neural network (which we name LightDefectNet) tailored specifically for light guide plate surface defect detection in resource-constrained scenarios was created via machine-driven design exploration with computational and "best-practices" constraints as well as L_1 paired classification discrepancy loss. Experiments show that LightDetectNet achieves a detection accuracy of ~98.2% on the LGPSDD benchmark while having just 770K parameters (~33X and ~6.9X lower than ResNet-50 and EfficientNet-B0, respectively) and ~93M FLOPs (~88X and ~8.4X lower than ResNet-50 and EfficientNet-B0, respectively) and ~8.8X faster inference speed than EfficientNet-B0 on an embedded ARM processor. As such, the proposed deep learning-driven workflow, integrated with the aforementioned LightDefectNet neural network, is highly suited for high-throughput, high-performance light plate surface VQI within real-world manufacturing environments.

本文介绍了一种高通量、高性能的深度学习驱动的光导板表面视觉质量检测工作流，该工作流使用紧凑的深度神经网络LightDefectNet实现自动化视觉质量检测。实验结果表明，所提出的工作流以及LightDefectNet神经网络非常适合在实际制造环境中进行高通量、高性能的光板表面视觉质量检测。

High-Throughput, High-Performance Deep Learning-Driven Light Guide Plate
  Surface Visual Quality Inspection Tailored for Real-World Manufacturing
  Environments

适用于实际制造环境的高通量、高性能的深度学习驱动光导板表面视觉质量检测