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)Classifying Overhead Images Taken by Diﬀerent Height Cameras
Predicted Depth by Fully Convolutional Residual Networks
with Convolutional Neural Network
Shu SUMIMOTO Yuichi MIYATA Ryuta YOSHIMURAYoko UWATE Yoshifumi NISHIO( Tokushima University )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 depth ofsome objects, such a human and chairs, in overhead images
with Fully Convolutional Residual Networks (FCRN). We
classify the AI-generated predicted depth images and weaim at diﬀerentiating human or other objects.

We define as the learning steps 300, the number of thetraining data sets 60 which are 40 overhead images of ahuman and 20 overhead images of a chair and the numberof the test data sets 10 which are 5 overhead images of ahuman and 5 overhead images of a chair. They are taken bya camera which is close to an object. Table 1 shows averageof learning and test accuracies when we classify only RGB
images 10 times and we classify only predicted depth images
10 times.
We define as the learning steps 100, the number of thetraining data sets 80 which are 40 overhead images of ahuman and 40 overhead images of a car and the numberof the test data sets 10 which are 5 overhead images of ahuman and 5 overhead images of a car. They are taken by acamera which is far from an object. Table 2 shows averageof learning and test accuracies when we classify only RGB
images 10 times and we classify only predicted depth images
10 times.
From Tables 1 and 2, when a camera is close to an object, the test accuracy of classifying RGB images is higher
than predicted depth images. On the other hand, when acamera is far from an object, the test accuracy of classifying
predicted depth images is higher than RGB images.

2. Proposed System
We propose to classify predicted depth images with
FCRN. First, we predict depth of overhead RGB imagesof a human, a chair and car taken by monocular cameras
in Figs. 1 and 2.
Second, we classify the RGB images and the predicted
depth images of a human and a chair or a car with a convolutional neural network which has 2 convolutional layers,
2 pooling layers and 2 fully connected layers.
We compare the learning and test accuracies when a camera is close to an object with when a camera is far from anobject. When they are close to a camera, we classify imagesof a human and a chair. On the other hand, when they are
far from a camera, we classify images of a human and a car.

Table 1: Average of learning and test accuracies
when a camera is close to an object.

Learning accuracy
Test accuracy

RGB
images
0.96500.87

Predicted depth
images
0.9016
0.72

Table 2: Average of learning and test accuracies
when a camera is far from an object.

Figure 1: Overhead RGB image.

Learning accuracy
Test accuracy

RGB
images
0.83350.38

Predicted depthimages
0.76490.58

4. Conclusion
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 of images.
However, the learning accuracies of classifying predicted
depth images are lower than RGB images. Then we will try
to raise accuracies of classifying predicted depth images.

Figure 2: Predicted depth image.

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