令和元年度電気関係学会四国⽀部連合⼤会 講演論⽂集 (2019 新居浜⾼専)
2019 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (NIIHAMA)

1-14Image Classification for Some Training Data Setsof Image from Overheadby Using Convolutional Neural Network
Shu SUMIMOTO

Yuichi MIYATA Yoko UWATE( Tokushima University )Yoshifumi NISHIO

1. Introduction

3. Simulation Results

Drones are recently infiltrating various fields, for example delivery, rescue, guard and so on. It is necessary thatdrones fly safely. Then image recognition by deep learning
is becoming important for that drones fly safely. However,standing people in overhead images from the view of drones
are not able to be recognized by using YOLOv3 which is anobject detection algorithm.
In this study, we investigate the prediction of the depthof some objects, such a human and chairs, in overhead images with Fully Convolutional Residual Networks (FCRN).
Further, we blend a RGB image and a depth prediction image by using OpenCV. Also, we process RGB images intoedge extraction images.
We classify the images of a human and a car which arecomposed of depth prediction images and edge extraction
images, blended images by using Convolutional Neural Network (CNN). We aim at diﬀerentiating human or other objects.

When the CNN learns 100 RGB images, the test accuracy
is 0.5 in Table 1. This value is very low, in other words theCNN can not classify a human and a car from overhead
view. From this result, we prepare other three data sets,
and compare thier test accuracies. First data set has 100
RGB images and 100 depth prediction images. Second data
set has 100 RGB images and 100 edge extraction images.
Third data set has 100 imgages blended by a RGB image
and a depth prediction image, and 100 images blended bya RGB image and an edge extraction image.
The test accuracies in Table 2 and 3 are higher value thanin Table 1, so we consider that it is eﬀective for the CNN toclassify images form overhead. Also, when the CNN learns
blended images, the test accuracy in Table 4 is higher thanin Table 1. From these results, the combination with RGB
imformation is important.

Table 1: Average of learning and test accuracies when the CNN

2. Proposed Systemlearn 100 RGB images.

We propose to classify images of a human and a car byusing a CNN. First, we process RGB images into depth
prediction images by using FCRN and edge extraction images, images blended by RGB images and depth prediction
images by using OpenCV in Fig. 1. Second, we classify
images of a human and a car with the CNN which has 2
convolutional layers, 2 pooling layers and 2 fully connectedlayers.
We compare the learning and test accuracies of each dataset. When the CNN learns, images are compressed. Therefore, training and test images are 32 × 32 pixels. The
learning rate of this the CNN is 0.00009.

Learning accuracy
Test accuracy

1.00
0.50

Table 2: Average of learning and test accuracies when the CNN
learn 100 RGB images and 100 depth prediction images.

Learning accuracy
Test accuracy

1.00
0.80

Table 3: Average of learning and test accuracies when the CNN
learn 100 RGB images and 100 edge extraction images.

Learning accuracy
Test accuracy

1.00
0.67

Table 4: Average of learning and test accuracies when the CNN

(a)

(b)

learn 100 imgages blended by a RGB image and a depth prediction image, and 100 images blended by a RGB image and anedge extraction image.

(c)

Learning accuracy
Test accuracy

1.00
0.73

4. Conclusion(d)

From these simulation results, we consider it is eﬀective
for classifying images taken by a camera which is far froman object to predict the depth and extract edge of images.
However, the test accuracies are still low, so we will makeother data sets based on combination with RGB information.

(e)Figure 1: Images of a human from overhead.
(a) A RGB image. (b) A depth prediction image.
(c) An edge extraction image.
(d) A image blended by a RGB image and a depth prediction
image.
(e) A image blended by a RGB image and an edge extraction
image.

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