Research on Intelligent Processing Technology of Computer Image
Recognition Based on Machine Vision
Li Xiong
1,*
and Mo Zhou
2
1
Dispatching Control Center of Guangxi Power Grid Corporation, Nanning, Guangxi, 530023, China
2
Tianjin University School of Civil Engineering, Tianjin, 300350, China
Keywords: Primary Visual Theory, Edge Detection Algorithm, Innovation and Entrepreneurship of College Students,
Education.
Abstract: With the continuous development of social technology, image recognition technology is widely used in
various fields. Therefore, the detection and processing technology of image recognition is particularly
important. Traditional detection technology can no longer meet the detection accuracy requirements of
computer image recognition intelligent processing technology, and the evaluation is unreasonable. Therefore,
this paper proposes an edge detection algorithm for innovative machine vision detection evaluation and
analysis. Firstly, the primary vision theory is used to evaluate the collected information, and the indicators are
divided according to the requirements of machine vision detection and evaluation to reduce the interference
factors in machine vision detection and evaluation. Then, the primary vision theory evaluates the machine
vision detection of computer image recognition, forms a machine vision detection evaluation scheme, and
comprehensively analyzes the evaluation results of machine vision detection. MATLAB simulation shows
that under certain evaluation criteria, the edge detection algorithm is better than the traditional detection
technology in the evaluation accuracy and machine vision detection
evaluation time of machine vision
detection for computer image recognition
.
1 INTRODUCTION
Image processing technology is one of the basic
operations of computer image recognition, but it
plays a very key role (Li, 2022). However, in the
process of machine vision inspection, the machine
vision inspection evaluation scheme has the problem
of poor accuracy (Longyang, and Yuefan 2019). In
order to improve the detection accuracy, some
scholars believe that the application of edge detection
algorithm to the analysis of computer image
recognition intelligent processing technology (Liu,
2019) can effectively analyze the machine vision
detection evaluation scheme and provide
corresponding support for the machine vision
detection evaluation (Shengying and Haixia 2019).
On this basis, this paper proposes an edge detection
algorithm to optimize the machine vision detection
evaluation scheme and verify the effectiveness of the
model (Li, 2019).
2 RELATED CONCEPTS
2.1 Mathematical Description of the
Edge Detection Algorithm
The edge detection algorithm uses automatic vision
theory to optimize the machine vision detection
evaluation scheme, and according to the indicators in
the machine vision detection evaluation, finds the
unqualified values in the computer image recognition
intelligent processing technology is
B
, and integrates
the machine vision detection evaluation scheme is
a
, and finally judges the feasibility of the computer
image recognition intelligent processing technology
is
()
i
Px
, calculated as shown in Equation (1).
()
i
P
xB a τ =−

(1
)
Xiong, L. and Zhou, M.
Research on Intelligent Processing Technology of Computer Image Recognition Based on Machine Vision.
DOI: 10.5220/0013540700004664
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 315-319
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
315

Among them, the judgment of outliers is shown in
Equation (2).
1
n
i
i
Ba Tn ττ
=
−= + +
∏

(2
)
The edge detection algorithm combines the
advantages of automatic vision theory and uses
computer image recognition intelligent processing
technology to quantify, which can improve the image
processing technology of machine vision detection
evaluation.
Suppose I. The evaluation requirements of
machine vision inspection is
y
, the machine vision
inspection evaluation scheme is
i
x
, the satisfaction of
the machine vision inspection evaluation scheme is
m
, and the judgment function of the machine vision
inspection evaluation scheme is
()
i
I
x
,as shown in
Equation (3).
()
i
I
x
y
m τ =−+

(3
)
2.2 Selection of Image Processing
Technology Solutions
Hypothesis II. The function of computer image
recognition intelligent processing technology is
()
i
A
s
, the weight coefficient is
ij
p
, then, the
machine vision inspection evaluation requires
unqualified computer image recognition intelligent
processing technology as shown in Equation (4).
()
ii ij
i
A
sx p ς =− +

(4
)
Based on assumptions I and II, a comprehensive
function of image processing can be obtained, and the
result is shown in Equation (5).
() ()
ii
Ix As B a τ +≤−

(5
)
In order to improve the effectiveness of machine
vision inspection evaluation, all data needs to be
standardized and the results are shown in Equation
(6).
1
() ()
n
ii i
i
Ix As T n τ
=
+↔++
∏
(6
)
2.3 Analysis of machine vision
inspection evaluation scheme
Before the edge detection algorithm, the machine
vision detection evaluation scheme should be
analyzed in multiple dimensions, and the machine
vision inspection evaluation requirements should be
mapped to the computer image recognition intelligent
processing technology library, and the unqualified
machine vision inspection evaluation scheme should
be eliminated is
()
ii
Z
a
. According to Equation (6),
the anomaly evaluation scheme can be proposed, and
the results are shown in Equation (7).
1
() ()
()
ii
ii
n
i
i
Ix As
Za
Tn
τ
=
+
=
++
∏
(7
)
Among them, it is
1
() ()
1
ii
n
i
i
Ix As
Tn
τ
=
+
≤
++
∏
,stated that
the scheme needs to be proposed, otherwise the
scheme needs to be integrated into it is
()
i
Sx
, and
the result is shown in Equation (8).
() min[ () ()]
iii
Sx Ix As=+

(8
)
The intelligent processing technology of
computer image recognition is comprehensively
analyzed, and the threshold and index weight of the
machine vision detection evaluation scheme are set to
ensure the accuracy of the edge detection algorithm.
The intelligent processing technology of computer
image recognition is a system test machine vision
inspection evaluation scheme, which needs to be
innovatively analyzed. If the computer image
recognition intelligent processing technology is in a
non-normal distribution, its machine vision detection
evaluation scheme will be affected is
()
i
D
x
,
reducing the accuracy of the overall machine vision
detection evaluation is
()
I
i
K
x
, and the calculation
result is shown in Equation (9).
INCOFT 2025 - International Conference on Futuristic Technology
316

min[ ( ) ( )]
( ) 100%
() ()
ii
Ii
ii
Ix As
Kx
Ix As
+
=×
+


(9)
The survey machine vision inspection evaluation
scheme shows that the image processing technology
scheme presents a multi-dimensional distribution,
which is in line with the objective facts. The
intelligent processing technology of computer image
recognition has no directionality, indicating that the
image processing technology scheme has strong
randomness, so it is regarded as a high analysis
research. If the random function of the computer
image recognition intelligent processing technology
is
cosh( )
i
ar x
, then the calculation of formula (9)
can be expressed as formula (10).
min[ ( ) ( )]
( ) 100% cosh( )
() ()
ii
I
ii
ii
Ix As
K
xarx
Ix As
+
=×+
+


(10)
Among them, the computer image recognition
intelligent processing technology meets the normal
requirements, mainly because the automatic vision
theory adjusts the computer image recognition
intelligent processing technology, removes the
duplicate and irrelevant schemes, and supplements
the default scheme, so that the dynamic correlation of
the entire machine vision detection and evaluation
scheme is strong.
3 OPTIMIZATION STRATEGY
OF INTELLIGENT
PROCESSING TECHNOLOGY
FOR COMPUTER IMAGE
RECOGNITION
The edge detection algorithm adopts the random
optimization strategy for the intelligent processing
technology of computer image recognition and
adjusts the parameters of the collected information to
realize the scheme optimization of the intelligent
processing technology of computer image
recognition. The edge detection algorithm divides the
intelligent processing technology of computer image
recognition into different machine vision detection
evaluation levels, and randomly selects different
schemes. In the iterative process, the machine vision
inspection evaluation scheme with different machine
vision inspection evaluation levels is optimized and
analyzed. After the optimization analysis is
completed, the evaluation level of machine vision
inspection of different schemes is compared, and the
best intelligent processing technology of computer
image recognition is recorded.
4 PRACTICAL CASES OF
INTELLIGENT PROCESSING
TECHNOLOGY FOR
COMPUTER IMAGE
RECOGNITION
4.1 Introduction to the Evaluation Of
Machine Vision Inspection
In order to facilitate the evaluation of machine vision
detection, this paper takes the intelligent processing
technology of computer image recognition in
complex cases as the research object, with 12 paths
and a test time of 12 h, and the machine vision
detection evaluation scheme of the specific computer
image recognition intelligent processing technology
is shown in Table 1.
Table 1: Machine vision inspection evaluation requirements
Scope of
a
pp
lication
Category Distinguish
the effect
Processing
effects
Profile
information
Human
identification
71.56 70.26
Automatic
identification
78.22 78.92
Texture
information
Human
identification
72.11 70.36
Automatic
identification
79.35 80.75
Image size
information
Human
identification
79.00 70.28
Automatic
identification
81.05 78.95
The machine vision inspection evaluation process
in Table 1. is shown in Figure 1.
Compared with traditional detection technology,
the machine vision inspection evaluation scheme of
edge detection algorithm is closer to the actual
machine vision inspection evaluation requirements.
In terms of the rationality and fluctuation range of
computer image recognition intelligent processing
technology, the edge detection algorithm is better
Research on Intelligent Processing Technology of Computer Image Recognition Based on Machine Vision
317

Visual system acquires
images
Pretreatment
Texture feature Contour feature
Computational
classification
Image processing
Output recognition result
Figure 1: Analysis process of computer image recognition
intelligent processing technology
than the traditional detection technology. Through the
change of machine vision inspection evaluation
scheme in Figure I, it can be seen that the stability of
the edge detection algorithm is better, and the
recognition speed is faster. Therefore, the machine
vision detection evaluation scheme of edge detection
algorithm, the speed of image processing technology,
the machine vision detection evaluation scheme, and
the summation stability are better.
4.2 Computer image recognition
intelligent processing technology
The machine vision inspection and evaluation
scheme of computer image recognition intelligent
processing technology includes non-structural
information, semi-structural information and
structural information. After the pre-selection of edge
detection algorithm, a preliminary machine vision
detection evaluation scheme of computer image
recognition intelligent processing technology is
obtained, and the feasibility of machine vision
detection evaluation scheme of computer image
recognition intelligent processing technology is
analyzed. In order to more accurately verify the
technological innovation effect of computer image
recognition intelligent processing, the intelligent
processing technology of computer image
recognition with different machine vision detection
evaluation levels is selected, and the machine vision
detection evaluation scheme is shown in Table 2.
Table 2: The overall situation of the image processing
technology solution
Cate
g
or
y
Resolution Realis
m
Profile
information
79.94 81.41
Texture
information
80.19 79.01
Image size
information
79.34 78.95
mean 79.98 81.27
X
6
45.26 44.13
P=5.27
4.3 Image Processing Technology and
Stability of Machine Vision
Inspection Evaluation
In order to verify the accuracy of the edge
detection algorithm, compared with the machine
vision inspection evaluation scheme of traditional
detection technology, the machine vision inspection
evaluation scheme is shown in Figure 2.
Figure 2: Image processing technology with different
algorithms
It can be seen from Figure 2 that the image
processing technology of the edge detection
algorithm is higher than that of the traditional
detection technology, but the error rate is lower,
indicating that the machine vision detection
evaluation of the edge detection algorithm is
relatively stable, while the machine vision detection
evaluation of the traditional detection technology is
uneven. The average machine vision inspection
evaluation scheme of the above two algorithms is
shown in Table 3.
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318

Table 3: Comparison of machine vision inspection
evaluation accuracy of different methods
Algorithm Image
processing
technolog
y
Magnitude
of change
Error
Edge
detection
algorithms
80.19 79.01 1.18
Traditional
detection
technolo
gy
73.31 77.06 3.75
P 41.26 45.37 4.11
It can be seen from Table 3 that the traditional
detection technology has shortcomings in image
processing technology and stability in terms of
computer image recognition intelligent processing
technology, and the computer image recognition
intelligent processing technology has undergone great
changes, and the error rate is high. The image
processing technology of the general result of the
edge detection algorithm is higher than the traditional
detection technology. At the same time, the image
processing technology of the edge detection
algorithm is greater than 80.19%, and the accuracy
has not changed significantly. In order to further
verify the superiority and effectiveness of the edge
detection algorithm, the edge detection algorithm is
generally analyzed by different methods, as shown in
Figure 3.
Figure 3: Edge detection algorithm: Image processing
technology for machine vision detection evaluation
It can be seen from Figure 3 that the image
processing technology of the edge detection
algorithm is significantly better than the traditional
detection technology, and the reason is that the edge
detection algorithm increases the adjustment
coefficient of computer image recognition intelligent
processing technology and sets the threshold of the
collected information to eliminate the machine vision
inspection evaluation scheme that does not meet the
requirements.
5 CONCLUSIONS
Aiming at the problem that the intelligent processing
technology of computer image recognition is not
ideal, this paper proposes an edge detection
algorithm, and combines the automatic vision theory
to optimize the intelligent processing technology of
computer image recognition. At the same time, the
innovation of machine vision detection evaluation
and threshold innovation is analyzed in depth, and the
collection of collected information is constructed.
Research shows that the edge detection algorithm can
improve the accuracy and image recognition speed of
computer image recognition intelligent processing
technology. Ensure the reliability and efficiency of
image recognition, and then play a diversified role in
people's daily work and life and bring more
convenience to them.
ACKNOWLEDGEMENTS
Supported by Science and Technology Projects of
Guangxi Power Grid Company (Grant
No.GXKJXM20220171)
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