IEEE Workshop on Nonlinear Circuit Networks
December 7-8, 2018

Building Datasets of Aerial Videos Using Drone andExtending Datasets with Logistic Map
Ryuta Yoshimura, Yoko Uwate and Yoshifumi Nishio
Dept. of Electrical and Electronic Eng., Tokushima University
2-1 Minami-Josanjima, Tokushima 770–8506, Japan
Email:{nakashima, uwate, nishio}@ee.tokushima-u.ac.jp

Abstract—The drone is noticed that it is possible to acquire thedisaster situation in detail and promptly when natural disasters
occur. The drone is easy to deploy in case of disasters and the costof pilot development can be reduced. At this stage, the rescuer
is searching for rescuee through a drone camera manually.
We aim to develop the automated discovery system using CNN,
so we constructed the data set which is necessary for learningof CNN. In addition, our method for extending the dataset is toadd chaotic noise to the learning data.
Fig. 1.

I. I NTRODUCTION
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 rescued by drone, suchas the discovery of the victims left behind in the hill, riverand mountainous areas. In June, 2018, Police in the United
Kingdom used a drone with a thermal imaging camera to finda semi-conscious man at the edge of a cliff. Further, the caseof a car crash in rural Lincolnshire proved the usefulness ofthe drone rescue. Police arrived on the accident scene after
dark, however it was unable to find the driver. They used adrone equipped with a thermal imaging camera and found him
unconscious in the groove. In the past year, the drone withthermal imaging cameras saved the lives of at least 15 victims
hidden from the emergency team’s vision due to darkness andobstacles. It is also able to find in burn buildings and disasterzones to find people hiding in smoke and dust. As search andrescue teams and firefighters continue to accept drone with athermal camera, the number is expected to increase over thenext few years [1].
The rescuer currently is searching for rescuee through adrone camera manually. We aim to develop the automateddiscovery system using convolutional neural network (CNN),
so we constructed the data set which is necessary for learningof CNN. First, we obtain data by using drone. Second, weextend data to add chaotic noise to. Finally, we investigate
recognition accuracy with dataset we make with noise.
II. N EURAL N ETWORK
Figure 1 shows the construction of neural network. The
information moves from the input nodes, through the hidden
nodes and to the output nodes. The most simplest neuralnetwork is a single-layer perceptron network, and the network
consists of a single layer of output nodes. The sum of theproducts of the weights and the inputs is calculated in each

Feedforward Neural Network.

node, and if the value is above some threshold the neuron
fires. Deep learning is a multilayered structured neural networkmodeled on human and animal cranial nerve circuits. Deeplearning is a type of machine learning model, and in thismodel you learn a classification method directly from images,movies, texts and sounds. CNN is one of the most common
algorithm in deep learning. CNN is particularly good at finding
patterns in images and recognizing objects, faces, and scenes.
By learning directly from image data using image classification by pattern, extraction of features by hand is unnecessary.
Along with the progress of GPU and parallel computing, CNN
has become a key technology for new development in thefield of automatic driving and face recognition. Applications
that require object recognition and computer vision, such asautomated driving vehicles and face recognition applications,rely heavily on CNN. CNN is used to confirm thousands ofpathological reports and visual cancer cells I am finding it.
CNN has also been adopted for automatic driving vehicles
and is used for object exploration and learning to distinguishbetween pedestrians and road signs [2]-[4].
III. P ROPOSED METHODFirst, we describe the data collection for data set construction. We obtained videos by letting the drones pass over thesky of the five targets. After that, we cut out the videos intoa frame image using OpenCV, and excluded the images inwhich the targets were not shown. We divided one-third of
the obtained images for testing and others for learning.
Figure 2 shows the some of the images we obtained usingdrone. There is a man who wears different color shirt which
are black, blue, white, yellow, white in the image. Finally, we
obtained 198 images for learning.

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(a)

(b)

(c)

(d)

eter value of the logistic map is changed. The logistic map is alogarithmic map in which the change in the value depends on
what value is given to the parameter a, converging to a constantvalue, cyclically repeating multiple values Vibrations, nonperiodic and extremely complicated behavior called chaos..
We set the values of a = 4.0 and a = 3.828327 in this study.
When a = 4.0, it shows a behavior close to a random number
called pure chaos.

Fig. 4.

(e)(a) wearing black shirt, (b) wearing blue shirt, (c) wearing
white shirt, (d) wearing yellow shirt, (e) wearing red shirt

Logistic map at the time when a = 3.828327.

Fig. 2.

Next, we added salt and pepper noise to increase the data.
The probability of applying noise to each image is the outputof the logistic map. It is known that random noise with auniform probability distribution is used. However, using thisnoise due to non-uniform output of this probability distribution
increases the robustness of the network and improves thelearning system expect. For that reason, noise caused by thelogistic map is used. The logistic map behaves like various
chaos depending on the parameters. The proposed method of
this research is to generate noise with nonuniform frequency
distribution and use this chaos. The formula of the logistic
map is as follows.
xn+1 = axn (1 − xn )TABLE IT HE PARAMETERS AND THE NUMBER OF IMAGES OF THE
LEARNING DATASET.
Parameter
I. basic(without noise)
II. a=4.0
III. a=3.828327
IV. a=4.0, a=3.828327
V. a=4.0(contained)
VI. a=3.828327(contained)
VII. a=4.0, a=3.828327(contained)(1)

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 frommoving images, so we expect each image to be affected by
the dynamics of this noise. If the value of the map outputs 0
to 1, it is set to 0 to 100%

Fig. 3.

Figure 4 shows a behavior of intermittency chaos whichis switching between laminar part and burst part. The time
range in which xn is causing irregular behavior and the time
range in which periodic behavior of three cycles are occurring
alternately. Laminar means the periodic state and burst means
the chaotic state. When we determine the parameter a =3.828327, the logistic map shows intermittency chaos [5]-[6].

Number of images
198
396
396
594
396
396
594

Table 1 shows the parameters and the number of imagesof the learning data used in this study. IV and VII are madeto learn two types simultaneously with noise added by eachparameter. V, VI, VII contain the noise application rate from 0
to 10 percent. We practiced the above-mentioned seven typesof noise application, we obtained the number of images asshown in Table 1. We obtained 396 pictures for II, III, IV andVI and 594 pictures for IV and VII.
Figure 5 shows one of the images added noise to. As you
can see, (a) and (b) are more dense noise than (c) and (d).
The reason for this is that the ratio of noise applied to c andd is contained in 0 to 10 percent.

Bifurcation diagram of logistic map.

Figure 3 shows vary complicated behavior when the param--2-

(a)

(b)

(c)

(d)

connected layer is the identification part that obtains the final
output by repeating the calculation.
In addition to the above, the important structure is thesoftmax function. Since the output of the neural network cantake an arbitrary numerical value, it must be converted to aprobability (a value of 0.0 to 1.0). The softmax function realizes faster and more efficient learning by mapping a negativevalue to zero and holding a positive value. This process is also
called activation, because only activated features are sent tothe next layer. The softmax function is useful in that case. Thesoftmax function is defined as follows.
exp(yi )pi = ∑C
j=1 exp(yj )Fig. 5. One of images due to (a) noise of II, (b) noise of III, (c) noise(2)of V, (d) noise of VI(a) Location(b) Drone(a) Sakura Park, Naka, Tokushima (b) holystone HS200【hs200Wr】
Fig. 6.

pi is the probability of becoming class i, y is the output ofthe neural network, and C is the total number of discriminantclasses. exp is an exponential function. In this study, thetotal number of discriminant classes is 5, so C is 5. For allclassification classes i, p satisfies 0 ≤p≤ 1 and the sum is 1.
CNN identifies and classifies features of images, letters,sounds, and movies. In this study, CNN extracts features by
convolution and pooling of the imported image and acquires
final output at full connected layer to recognize it.
Figure 7 shows the structure of CNN in this study. Thismodel consists of an input layer, two convolution layers, twopooling layers, two full connected layers and an output layer.

Figure 6 shows the location at the time of data collectionand photographing equipment. We obtained videos in Sakura
Park, Naka-cho, Tokushima-city, Tokushima-Prefecture. The
weather at that time was cloudy. We used the drone which is
HS200 from Holystone as a photographic equipment.
IV. C ONVOLUTIONAL NEURAL NETWORKIn this research, we used a convolutional neural network(CNN) widely used for image and video recognition. TheCNN consists of dozens of layers, each layer learning todetect different features of one image. For each learning image,multiple filters with different resolutions are applied and theoutput of each convolution image is used as input to thenext layer. Filters begin with the detection of very simple
features such as brightness and edges and increase the levelof complexity of features that uniquely define objects.
As with other neural networks, CNN consists of an input
layer, an output layer, and many hidden layers in between, eachlayer learning features unique to the data. CNN is generallycomposed of convolutional layers and a pooling layer, fully
connected layers. The convolution layer and the pooling layer
are feature amount extraction parts that read images and create
feature maps.
The pooling layer performs nonlinear down sampling tosimplify the output while also reducing the number of parameters required to learn the network. These processes arerepeated for dozens or hundreds of layers, learning is done
so that each layer distinguishes different features. The fully

Fig. 7.

Flow of learning by CNN.

V. S IMULATION RESULTS
We investigated learning accuracy by learning these datawith CNN and how noise influences learning accuracy. The
number of learning is 1000 times. Table 2 shows learning
accuracy with various parameters.
TABLE IIIIIIIIIVVVIVII

L EARNING ACCURACY.
step=100 step=200 step=500
0.30800.65150.75250.33330.6136
0.87120.41540.64970.85500.32800.59460.81860.53460.84480.98090.55800.93620.984
0.59690.91211.0000step=1000
0.7626
0.88630.86230.85170.98800.98861.0000The learning accuracy improves as the number of times oflearning increases. When the number of steps is 200, VI is the-3-highest training accuracy, and VII is the best after 500 steps.
As VII converges to 1, it is a possibility of over learning.
VI. C ONCLUSIONSWe did not do left/right flipping, zooming, shrinking, androtation of the image, which is said to be a general way toextend the number of images at this time. Instead, we extended
the dataset by adding noise to the image. As a result, thelearning accuracy was successfully improved by increasing
the number of images. Learning accuracy was improved byincreasing the number of data due to adding noise. However,when some of results in learning accuracy converge 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 salt and pepper noise. Also, inconsidering the results, it is difficult to investigate which image
data was useful for accuracy. This is because it depends on
the structure of CNN that we do not know which feature ofimage the inference results were made. In order to build amore robust data set in the future, we will collect more dataand build a data set that extended them and learn it.
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