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Light guide plates are essential optical components widely used in a diverserange of applications ranging from medical lighting fixtures to back-lit TVdisplays. In this work, we introduce a fully-integrated, high-throughput,high-performance deep learning-driven workflow for light guide plate surfacevisual quality inspection (VQI) tailored for real-world manufacturingenvironments. To enable automated VQI on the edge computing within thefully-integrated VQI system, a highly compact deep anti-aliased attentioncondenser neural network (which we name LightDefectNet) tailored specificallyfor light guide plate surface defect detection in resource-constrainedscenarios was created via machine-driven design exploration with computationaland "best-practices" constraints as well as L_1 paired classificationdiscrepancy loss. Experiments show that LightDetectNet achieves a detectionaccuracy 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 anembedded ARM processor. As such, the proposed deep learning-driven workflow,integrated with the aforementioned LightDefectNet neural network, is highlysuited for high-throughput, high-performance light plate surface VQI withinreal-world manufacturing environments.

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