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.
翻译:在这项工作中,我们引入了一种完全集成、高通量、高性能、高性能的深层次学习驱动工作流程,用于光导板表面视觉质量检查(VQI),以适合现实世界的制造环境。为使全集成VQI系统中边缘计算中的VQI自动化VQI,一个高度紧凑的、防爆的深层凝固神经网络(我们命名为LightDefectNet),专门为在资源控制情景中进行光导板表面缺陷探测而专门设计的,这是通过机器驱动的设计探索,通过计算和“最佳做法”的制约以及L_1对齐分类差异损失进行。实验显示,光检测网在完全集成的VGPSDD基准中实现了~98.2%的检测精确度,同时只有770K参数(分别比ResNet-50和高效的Net-B0低,),以及以资源控制情景组合情景下的光线性FLOP(~88X),在RENet-Nender-net-net-Silential Streal Streal-delex,在快速学习中比ResNet-deal-deal-50和快速进行。