Detecting Tower Crane with Multi-features

Haiming Yin

College of Mathematics Physics and Information Engineering， Jiaxing University， Jiaxing

Zhejiang 314001， China

haimingyin_hawk@hotmail.com

Keywords: Tower Crane; Feature Extraction; Object Detection

Abstract. In this paper, we present a new algorithm for tower crane detection in images with

multi-feature analysis. The image will be segmented into several objects first, and then for each

object, its color, texture and geometric features are extracted, and at last, all features extracted are

fed to a trained SVM to generate the final judgment. Experiments show that our algorithm can

detect tower crane in images with high recall and precision.

Introduction

The tower crane can be seen everywhere in the modern work site, and it is the essential

mechanical equipment to the construction. But in some occasions, such as near the high-voltage

wire, the tower may be a threat to the safety of cable. There must be a set of solutions to keep the

tower crane far from the high-voltage wire, the most critical steps is to detect and track the tower

crane’s position accurately. In this paper we address the problem of tower crane detection.

Color, texture and shape are the three main characteristics of an object. They are widely applied

in the extraction of low-level visual features. People usually find the interest regions by color

features, then identify the objects according to their texture and shape. Combing these three features

in describing the object has become the consensus of researchers. In this paper, we present

a crane detection method combining color, texture and shape features; the algorithmic process is

shown as figure 1.

input image

image

segmentation

color feature

extraction

texture feature

extraction

geometric feature

extration

features analysis

detection results

Fig. 1 The algorithm flow chart

The rest of the paper is organized as follows: section 2 introduces image segmentation, section

3 addresses feature extraction, section 4 introduces the judgment with SVM, section 5 gives out the

experimental results and the last part of the paper is a brief summary our work.

Applied Mechanics and Materials Vols. 488-489 (2014) pp 854-857 Online: 2014-01-08© (2014) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMM.488-489.854

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Image segmentation

Since our tower crane detection algorithm is based on object feature analysis, the image must

be segmented into objects before the analysis work. Image segmentation is the process to divide the

whole image into a number of specific regions according to some kinds of rules. It’s the key step

from image processing to image analysis. The existing image segmentation methods can be roughly

classified into the following categories: threshold-based methods [1], region-based methods [2],

edge-based methods [3], and some other specific segmentation method [4]. The method proposed

by Y.Deng et al. can get good performance with either texture images or natural image

[5]. The tower crane object is a multi-texture object, and the site background is similar to a natural

scene, so this paper adopts this method for the segmentation. The experimental results show

the segmentation can achieve good results when dealing with tower crane images.

Feature extraction

Color feature extraction. Since tower is a kind of special instruments, it has its own warning

color; usually the tower cranes look yellow or deep yellow. We can make full use of

these characteristics in the detection process. Because of the change of the image shooting

environment, tower crane color may change greatly, we analyzes the hue component in the HIS color

space. Since the source is commonly a RGB image, it should be converted to HIS color space and

we do the converting using the Eq. 1.

1 2[90 arctan( ) {0, ;180, }]

360 3( )

min( , , )1

3

R G BH G B G B

G B

R G BS

I

R G BI

− −= − + > <

−

= −

+ +=

（1）

Through the analysis of a large number of experimental data, we found that the H value of

tower cranes vary from 0.24 to 0.49.

Texture feature extraction. Texture is a small, semi periodic or regularly arranged pattern in a

certain range of an image. It is usually used to represent the consistence and rough degree and such

on properties. Texture refers to the change of image gray level, and this change is closely associated

with the spatial statistics. An object is usually with stable texture. And generally extracting texture

features of objects using statistical methods can obviously reduce the computation complexity [6,

7]. The texture of tower crane has the following features: 1) the symmetry is strong; 2) it is mainly

composed of latticed structures; 3) there is strong horizontal and vertical gradients and

large variance in the texture regions. The object texture features are computed by Eq. 2- Eq.5.

Let σ be a given region, N is the pixel num in the region, p

G is the gray value of point p, || ||

means the modulus.

1p

p

m GN σ∈

= ∑ (2)

21

1 ( )p

p

G mN σ

δ∈

= −∑ (3)

2

( , ) ( 1, )

12 || ||x y x yG G

N σ

δ+

= −∑ (4)

2

( , ) ( , 1)

13 || ||x y x yG G

N σ

δ+

= −∑ (5)

We use 1δ ， 2δ ， 3δ to represent the tower crane’s texture feature.

Applied Mechanics and Materials Vols. 488-489 855

Geometric feature analysis. The geometric feature is another important feature of an

object, which mainly includes object perimeter, area and shape. Since the varying in size caused by

the different distance, we can’t use the perimeter and area as the measure directly. We use

the relative value S between perimeter and area to represent one of the geometric

features. Considering the linear characteristics of the tower crane, we also detect the lines in the

object.

Let A be the area and L be the perimeter of an object, the S value is calculated by Eq. 6.

AS

L=

(6)

We use the Hough transform to detect the line, and get the statistics of all kinds of line. In our

experiments, we count the num of long, middling and short lines (the specific length boundary is

adjusted according to the object size), represented as C1, C2, C3.

Object identifying

With the development of artificial intelligence, machine learning has been widely used to

the feature analysis and object identification in [8], and in most cases, it achieved good

results, especially in the face detection application [9]. We use the popular SVM to discriminate the

tower cranes from various objects. Support vector machine (referred to as SVM) effectively solve

the common problems in the classical learning method such as dimension disaster, local minima

[10]. The basic principle is to use nonlinear mapping to map a low dimensional vector to a high

dimensional inner product space, and linearly divides the features in the high dimensional. The

biggest problem of SVM is its high computation complexity, but now people can solve this problem

by selecting an appropriate kernel function. The kernel functions in SVM which have been widely

studied include: radial basis function (RBF), polynomial and multilayer Sigmodial neural

network. In the experiments, we RBF as the kernel function, it is shown in Eq. 7. 2

2

| |( , ) exp{ }

x yK x y

σ

−= −

(7)

When processing an image, we normalize the image size first, and objects are segmented out

from the image, then the values of H, δ1, δ2, δ3, S, C1, C2, and C3 are computed which form an

eight dimensional vector, at last the vector is fed to the trained SVM and the detection result is

achieved.

Experimental results and analysis

In our experiments, the data set is composed of 400 tower crane images and 1000 other images.

We use half of the data set to form a blend library to train the SVM and use the rest of the images as

test data. The experimental results indicate our algorithm can detect tower crane from image

effectively with high precision and recall. The test results are shown in the Tab. 1.

Tab.1 Test results

image type tower crane common image

picture num 200 500

alarm num 178 21

In the experiments we also found that in some images with complex background, the tower

crane could not be segmented out clearly which caused most of the misdetection. The false alarms

are mainly caused by the Artificial building such as rows of windows of residential area.

856 Materials Science, Civil Engineering and Architecture Science, MechanicalEngineering and Manufacturing Technology

Conclusions

According to the visual features of the tower crane, we present a SVM-based detection method

which combines multi feature. First, image is segmented into objects, and then color feature, texture

feature and geometric feature are extracted and fed to a trained SVM, at last the SVM give out the

detection result. Because of the full use of all kinds of information of the tower crane, our algorithm

gets high recall and precision. The future work is to improve the accuracy of object

segmentation, and reduce the false alarm caused by artificial construction.

Acknowledgements

This work was financially supported by the Zhejiang province commonweal projects

(2012C21020), and Jiaxing Science and technology project (2012AY1026).

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