Journal of Robotics, Networking and Artificial Life, Vol. 2, No. 2 (September 2015), 100-102

Target Recognition based on Machine Vision for Industrial Sorting RobotJiwu Wang1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Huazhe Dou 1, Shunkai Zheng1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Sugisaka Masanori 2

Alife Robotics Corporation Ltd, Japan and Open University, United Kingdom2
E-mail: jwwang@bjtu.edu.cn; ms@alife-robotics.co.jp
Abstract
In order to improve the applications for an industrial sorting robot, it is necessary to increase its flexibility andcontrol accuracy. Here an industrial robot arm is designed and set up for experiment simulation with machine
vision. Some target recognition experiments are designed for vision recognition. The flying badminton tracking
experiment is given to verify the efficiency of the developed algorithm. Moreover, the multiple target recognition
are also tested with our developed algorithms. The machine vision technology is an effective solution. In order toreduce the influence of the size, deformation, and lighting etc., the target recognition and location method withfusion of scale invariant feature transform (SIFT) and moment invariants is developed. The experiments results
showed that the developed image processing algorithms are robust, and the flexibility of the industrial robot can be
improved by machine vision.
Keywords: Arm robot, Target recognition, Flexible control, Machine vision

1. Introduction
Intelligent industrial robot, as the concentration andintegration of advanced manufacturing technology,
information technology and network technology etc., isgetting more and more attentation. 1 This is because a lotof robots and automated equipments will be required in
the future applications.
Now, most of the traditional industrial robots can only
work according to some fixed paths by an operator’sprogramming. The position of each target ispredetermined, and robots only perform repetitiveactions. But in the applications of the automatedproduction line or a flexible manufacturing system,2such simple operations are not always satisfied to thepractical request. It is necessary to obtain the position ofeach target automatically, and then adjust the angle ofeach joint correspondingly. Increasing industrial robot’sflexibility and improving its repetitive positioning
accuracy is the focus for their further applications. 3 In

order to realize above functions, the prerequisite is toget the target position accurately and reliably.
Machine vision is a good solution. A CCD camera canprovide more information than other sensors. And somepattern recognition algorithms can be used to extract thetarget and calculate its position automatically in one
captured image. Moreover, multiple targets and theirpositions can be determined at the same time, whichmakes robots to be flexibly controlled possible.
In order to get the position of each target, the target
should be recognized first. Due to the influence oflighting, size, rotation and small scale perspective
transformation etc., some target recognition algorithms
are not robust. It will cause target positioning unreliable.
The SIFT algorithm can effectively improve target
recognition capability. In this paper, the SIFT algorithm
is used for target recognition, and the target position iscalculated with moment invariant method.

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Jiwu Wang, Huazhe Dou, Shunkai Zheng, Sugisaka Masanori

2. Target Recognition Based on Image Matching
For pattern recognition, some scale and grayscaletransformation can not be avoided. It will make somematching algorithm unreliable. The algorithm of thescale invariant feature transform4 (SIFT) is a local
feature detection algorithm. It is based on matching
image feature points to realize target recognition. Evenin a complex environment, the target can be quickly andaccurately recognized.
The kernel of SIFT algorithm is to search key points inthe different scale space, and calculate their direction.
The feature points in SIFT are those points, such ascorners, edges, dark spot and bright spot etc., which will
not be affected by illumination, viewpoint or occlusion.
The process of SIFT algorithm is shown in figure 1.

Fig.3. The target recognition of flying badminton

Another experiment is shown in figure 4.

Fig.4 Illustration on the matching results
Fig.1.Illustration of SIFT algorithm steps

Image recognition is to identify the target image withthe source images in the sample library. Compared withother pattern recognition algorithms, the necessary stepis to input the sample target image in the sample library,which simplifies the complex operation for image
processing. The target recognition process is shown infigure 2.

In figure 4, the scale and orientation transformation of
the target are studied. Moreover, the influence ofviewpoint and brightness modification is studied too. As
shown in figure 4, with the SIFT algorithm, thematching results are robust and reliable. For each
picture in this group, the left side is the input image tobe detected, and the right side is the sample library.
Their relationship is shown by connecting lines between
the extracted feature points.

Fig.2. The flow chart of target recognition

In order to verify above algorithms, the following
experiments of flying badminton tracking are carried. In
figure 3, the red mark is the position of the flying
badminton.

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Target Recognition based on Machine Vision

figure 5, each target area is marked with a red cross.
Their coordinates are also given in unit of pixel andmillimeter.
3. Experiments
Based on the above target positioning with image
processing algorithms of SIFT and moment invariants,
the positioning control experiment with our arm robot iscarried on. The CCD camera is fixed in the arm robot
coordinate system. The image processing results are
similar to figure 5. Thus the target position can be easilytransformed to the end pose of the manipulator. With
inverse kinematics equations, the angle of each joint canbe calculated.
The experiment results show that the flexible control for
multiple target operations can be performed with image
processing methods developed in my lab. Moreover,with SIFT and moment algorithms, the influence oflighting and scale is low to the target position
calculation.

1) The source image

4.

Conclusions

In this paper, an arm robot and the position control
system based on a CCD camera is developed. Multiple
target control operations (like in figure 5) are performed
with our lab arm robot. By algorithm fusion of SIFT andmoment invariants, the multiple targets can be extracted
robust and their positions can be calculated accurately.
Moreover, it can effectively control the influence oflighting and scale etc.

2)The result with positioning

References

3) The corresponding coordinates
Fig.5 Illustration on the target positioning

After the target is extracted by matching with SIFT
algorithm, the next step is to determine its position.
Generally, the center is necessary for each object, so themoment invariant algorithm is developed. As shown in

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