image analysis, video surveillance, virtual reality and
augmented reality, intelligent driving assistance and
agricultural pest hazard detection and other fields.
Face recognition technology is the use of
computer algorithms, through the analysis of facial
features, to achieve automatic recognition and
classification of faces. This technology is widely used
in road safety, finance and other fields, such as access
control systems and mobile payment. Object
detection is the use of computer vision technology to
realize the automatic detection and recognition of
objects in images. This technology is used in
intelligent construction, smart home intelligent
security, and other aspects such as defect detection on
automated production lines and intelligent vacuum
cleaners. Scene classification is to automatically
classify and label images according to the scene
information in the image. Scene classification
technology is reflected in intelligent photo albums,
intelligent search engines and other software, such as
automatic classification of photos according to the
scene, intelligent recommendation related content.
Image content editing is to realize the automatic
editing and modification of image content. Image
content editing is mostly used in image programming
software such as Meitu Xiuxiu and Photoshop, which
can automatically repair photo defects and realize
intelligent whitening. Image recognition technology
is widely used in the field of intelligent transportation.
For example, traffic surveillance cameras can learn
vehicle identification, vehicle counting and vehicle
violation identification through image recognition
technology, so as to improve the efficiency and
accuracy of traffic management. In the field of
medical imaging, image recognition technology can
automatically analyze and recognize medical image
images to help doctors make early diagnosis and
treatment of diseases. For example, the early
detection of breast cancer can automatically identify
potential tumor areas through image recognition
technology to improve the speed and accuracy of
diagnosis. In the field of security monitoring, security
cameras can realize human face recognition, behavior
recognition and other functions through image
recognition technology, helping monitors to detect
anomalies in time and provide effective security
warnings. In the field of Unmanned Aerial Vehicle,
image recognition is mainly used for target tracking,
terrain recognition and so on. UAV can automatically
track the target object, carry out real-time shooting
and monitoring, and carry out ground recognition to
realize the autonomous navigation and flight control
of UAV. In intelligent robots, image recognition
technology is an indispensable part of robot
intelligence. Robots can identify objects and people
in the environment for autonomous navigation, target
tracking, human-computer interaction and other
tasks. In addition, in the field of service robots, robots
can improve the service quality of robots in terms of
face recognition and emotion recognition. At present,
there are robots in major amusement parks to identify
players in real time through cameras. They can also
analyze facial expressions to achieve emotional
interaction and improve the game experience. The
intelligent inspection robot of the substation monitors
and maintains the power equipment through the
ability of image recognition technology, such as
autonomous perception, autonomous planning,
autonomous execution and autonomous learning,
such as abnormal oil level of a transformer, excessive
winding temperature, insulation damage, instrument
aging and fouling. The intelligent inspection robot
can independently analyze the corresponding
decision-making according to the problems that arise.
2.3 The Characteristics and
Classification of Image Recognition
Technology Algorithm
The traditional algorithms of image recognition
technology include: edge detection, morphological
processing, linear fitting and other algorithms.
Edge detection is used to identify significant
changes in the image, usually representing the
boundary of the object. Instrument detection can be
used to identify the edge of the scale line.
Morphological processing This is a method of
analyzing shapes. Some shape features can be
extracted or enhanced from the image through
morphological processing. For example, it can be
used to remove noise, connect broken lines or find
objects of a specific shape. Line fitting : This may be
necessary for recognizing lines (e.g., scale lines) in an
image. The algorithm can determine which points
should be regarded as a straight line and give its
parameters (such as slope and intercept).
The CNN algorithm can directly use the
instrument image as input, output the dial, pointer and
scale after feature extraction and feature mapping, or
output the indicator directly.
Image recognition technology algorithms are
divided into two categories of algorithms: one-stage
and two-stage.
The Faster R-CNN algorithm belongs to the two-
stage algorithm. Its structure mainly includes
convolution layer, RPN layer, region of interest
pooling layer and classification regression layer.
Faster R-CNN has superior performance, high-
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