Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LightDefectNet: A Highly Compact Deep Anti-Aliased Attention Condenser
  Neural Network Architecture for Light Guide Plate Surface Defect Detection   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. An essential step in the manufacturing of light guide plates is the
quality inspection of defects such as scratches, bright/dark spots, and
impurities. This is mainly done in industry through manual visual inspection
for plate pattern irregularities, which is time-consuming and prone to human
error and thus act as a significant barrier to high-throughput production.
Advances in deep learning-driven computer vision has led to the exploration of
automated visual quality inspection of light guide plates to improve inspection
consistency, accuracy, and efficiency. However, given the cost constraints in
visual inspection scenarios, the widespread adoption of deep learning-driven
computer vision methods for inspecting light guide plates has been greatly
limited due to high computational requirements. In this study, we explore the
utilization of machine-driven design exploration with computational and
"best-practices" constraints as well as L$_1$ paired classification discrepancy
loss to create LightDefectNet, a highly compact deep anti-aliased attention
condenser neural network architecture tailored specifically for light guide
plate surface defect detection in resource-constrained scenarios. Experiments
show that LightDetectNet achieves a detection accuracy of $\sim$98.2% on the
LGPSDD benchmark while having just 770K parameters ($\sim$33$\times$ and
$\sim$6.9$\times$ lower than ResNet-50 and EfficientNet-B0, respectively) and
$\sim$93M FLOPs ($\sim$88$\times$ and $\sim$8.4$\times$ lower than ResNet-50
and EfficientNet-B0, respectively) and $\sim$8.8$\times$ faster inference speed
than EfficientNet-B0 on an embedded ARM processor.

18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
3
The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat