平成 30 年度電気関係学会四国支部連合大会 講演論文集 (2018 愛媛大学)

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2018 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (EHIME)

Building Datasets of Aerial Videos Using Drone andExtending Datasets with Chaos Noise
Ryuta YOSHIMURA Yoko UWATE Yoshifumi NISHIO(Tokushima University)
1. Introduction

The table shows the parameters and the number ofimages of the learning data used in this study.

The drone is noticed that it is possible to acquire thedisaster situation in detail and promptly when natural
disasters occur. Last year, 124 people were rescuedby drone, such as the discovery of victims left behindin the hill, river and mountainous areas. The drone
is easy to deploy in case of disasters and the cost ofpilot development can be reduced. At this stage, therescuer is searching for rescuee through a drone camera
manually.
We aim to develop the automated discovery systemusing CNN, so we constructed the data set which isnecessary for learning of CNN. In addition, our method
for extending the dataset is to add chaotic noise to thelearning data.

Table 1: The parameters and the number of images of thelearning dataset.
Parametera. basic(not to add noise)b. a=4.0
c. a=3.828327
d. a=4.0, a=3.828327
e. a=4.0(contained)
f . a=3.828327(contained)
g. a=4.0, a=3.828327(contained)

Number of images
198
396
396
594
396
396
594

d and g are made to learn two types simultaneouslywith noise added by each parameter. e, f , g containthe noise application rate from 0 to 10 percent.
In this research, we used a convolution neural network (CNN) widely used for image and video recognition. CNN extracts features by convolution and pooling of the imported image and acquires final output atfull connected layer to recognize it. This model consistsof an input layer, two convolution layers, two poolinglayers, two full connected layers and an output layer.

2. Proposed method
First, we describe the data collection for data setconstruction. We obtained videos by letting the drones
pass over the sky of the five targets. After that, we
cut out the videos into a frame images using openCV,and excluded the images in which the targets were notshown. We divided one-third of the obtained images
for testing and others for learning. Next, we added salt
and pepper noise to increase the data. The probabilityof applying noise to each image is the output of thelogistic map. The formula of the logistic map is asfollows.
xn+1 = axn (1 − xn )(1)

3. Simulation results
We investigated learning accuracy by learning thesedata with CNN and how noise influences learning accuracy. The number of learning is 1000 times. Table 2
shows learning accuracy by various parameters.

n means the number of steps. When xn is determined, the next value xn+1 is uniquely determined.
Similar to xn and xn+1 , there is continuity also in images extracted from moving images, so we expect each
image to be aﬀected by the dynamics of this noise.
The logistic map takes various values depending on thevalue of the parameter a. We set the values of a = 4.0
and a = 3.828327 in this study. When a = 4.0, it showsa behavior close to a random number called pure chaos.

Table 2: Learning accuracy.
abcdefg

step=1000.3080810.333333
0.415459
0.328076
0.534606
0.558087
0.596970

step=200
0.651515
0.613636
0.649758
0.594637
0.844869
0.936219
0.912121

step=500
0.752525
0.871212
0.855072
0.818612
0.980907
0.9840551

step=1000
0.762626
0.886364
0.862319
0.851735
0.988067
0.988611
1

The learning accuracy improves as the number oftimes of learning increases. When the number of steps
is 200, f is the highest training accuracy, and g is thebest after 500 steps.

4. Conclusions

Figure 1: Logistic map at the time when a = 3.828327.

Learning accuracy was improved by increasing data
due to noise. However, when learning accuracy converges to 1, it may be over learning.
In the future, we will investigate the generalization
ability and judge the existence of over-learning. We
will also investigate other parameters and sesame saltnoise.

Figure 1 shows a behavior of intermittency chaos
which is switching between laminar part and burst
part. Laminar means the periodic state and burstmeans the chaotic state. When we determine the parameter a = 3.828327, the logistic map shows intermittency chaos.

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