Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uneven illumination surface defects inspection based on convolutional
  neural network   Surface defect inspection based on machine vision is often affected by uneven
illumination. In order to improve the inspection rate of surface defects
inspection under uneven illumination condition, this paper proposes a method
for detecting surface image defects based on convolutional neural network,
which is based on the adjustment of convolutional neural networks, training
parameters, changing the structure of the network, to achieve the purpose of
accurately identifying various defects. Experimental on defect inspection of
copper strip and steel images shows that the convolutional neural network can
automatically learn features without preprocessing the image, and correct
identification of various types of image defects affected by uneven
illumination, thus overcoming the drawbacks of traditional machine vision
inspection methods under uneven illumination.

26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
3
The categories of this paper are: physics