Instrumentation & Measurement Magazine 24-4 - 67

and configures the console cabinet to provide the human-computer
interaction interface. Image processing equipment can
be implemented in spot industrial computers or embedded
processors, such as programmable gate array and applicationspecific
integrated circuits (ASIC). Among these, embedded
processors are more widely used owing to their lower cost.
The operation unit is used to process products according to
the data measured by the image processing system. Thus far,
the entire VBM system has completed an automated product
processing process.
Fig. 3. Container number recognition system. (a) Camera; (b) Captured image.
VBM Systems: VBM has been used for several years in factories
and production facilities for the inspection of equipment
or products and the detection of their properties; it is also
known as no contact or non-invasive measurement. The typical
structure of VBM systems in a factory is presented in Fig. 4.
A typical VBM system has three parts: an image capturing system,
the image processing system, and an operation unit.
An image capturing system is used to convert the image
into data that a computer can understand, process, and
send to the image processing device. This system usually
has two parts: camera and auxiliary equipment. Cameras are
used for acquiring image data, and they are classified according
to the different types of recognition tasks, such as visible
light cameras, infrared cameras, and laser scanners. Auxiliary
equipment includes tools that provide support for image data
collection and transmission such as image grabbing card and
lighting equipment.
An image processing system is used to obtain measured
data from the image information. Generally, this type of system
assumes the image processing equipment to be the core
VBM Algorithms: The VBM system has the following two main
functions: target detection and measurement. Target detection
refers to the process of approximating the location of the target
in the picture, whereas target measurement refers to measuring
the geometric characteristics or position data of the target.
Target Detection Algorithm
The target detection algorithm is used to identify the target
position in the image. Classical target detection algorithms
often use classifiers to detect the target, among which the recognition
pattern of histogram of oriented gradient (HOG) +
support vector machines (SVM) [7] is the most extensively
used. For improved speed of target recognition, traditional
target detection algorithms would use a feature description
algorithm to reduce the amount of information in the picture.
HOG is a feature descriptor used for target detection in image
processing, and it was first presented by Dalal and Triggs [8].
It calculates the gradient direction histogram of the image to
form a feature. Fig. 5 describes the image features described by
the HOG algorithm. After describing the image features with
the HOG operator, feature classifiers such as SVM are used to
classify the features.
Fig. 4. Typical VBM system in a factory.
June 2021
IEEE Instrumentation & Measurement Magazine
SVM is a supervised
machine learning algorithm
that can be employed
for both classification and
regression purposes, and
Cortes and Vapnik (1995)
first proposed SVM in 1995
[9]. It has proven to be superior
in solving small
data sets, and nonlinear
and high dimensional pattern
recognition problems.
The main objective of SVM
is to find the optimal hyperplane
H to accurately
distinguish between positive
samples (the target that
requires detection, such as
the container handle) and
negative samples (other
targets). Fig. 5c describes
the process of using SVM
to classify the features of a
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