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)

Investigation of Training Accuracy in Cut Out Images
Using Convolution Neural Network
Yuichi MIYATA

Ryuta YOSHIMURA Yoko UWATE( Tokushima University)

1. Introduction
In recent years, wild animals such as deer and boars
are rapidly increasing in Japanese forests. So, agricultural crops damaged in nearby farms. However, eﬀective control is limited. By using the camera loaded inthe drone, it is eﬀective for the detection and monitoring of wild animals in the forests. In order to realize
this, it is important to recognize animals in images captured in the forests.
In this study, we recognize images by using convolution neural network (CNN). We compare with digitallyprocessed images which are similar color cut out images
and original images for deep learning.

Yoshifumi NISHIO

CNN. When step is 0 to 40, the training accuracy ofdigitally processed images reached 1 earlier than images before processing. In addition, digitally processedimages obtains a little bit better test accuracy thanimages before processing when we learned images oftwo patterns (Table 1). However, test accuracy is notgood. It is necessary to increase accuracy when data
sets increased.

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2. Proposed system
CNN used for image recognition consists of input layers, convolutional layers, pooling layers, fully connectedlayers and output layers. There are two layers in convolutional layers, pooling layers, fully connected layers
for each one. Its function is to progressively reduce
the spatial size and length output from convolutionallayers and reduce the amount of parameters and calculation in the network. Especially, Spatial Pyramid
Pooling(SPP) can obtain fixed length output regardless of input image size. In addition, it is possible toimprove the accuracy of CNN of various structures byusing SPP in the pooling layer [1].
We prepare data set like Figs. 1 and 2 for learningand test. Figure 2 shows the threshold processing ofFig. 1 and cutting out the background (green). We
learned these by using CNN and investigate the accuracy of color cut out images and original images.

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Figure 3: Training accuracy of two pattern.

Table 1: Average of training and test accuracies.
Training accuracy
Test accuracy

Original images
0.99
0.42

Processed images
1.00.48

4. Conclusion
In this study, we compared with digitally processed
images which are similar color cut out images and original images for deep learning. We investigated learning
accuracy of color cut out images and original images.
We succeeded in cutting the green background in images used for learning. Therefore, we considered thatthe object becomes clear and increases the accuracy.
As our future works, we improve a pooling layer in
CNN and would like to obtain better test accuracy.
Figure 1: Original image.

Figure 2: Processed image.

5. Reference[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian
Sun, Spatial Pyramid Pooling in Deep Convolutional
Networks for Visual Recognition , Computer Vision
ECCV, 13th European Conference, Zurich, Switzerland, September 6-12, 2014, proceedings, Part, pp.346361.

3. Simulation results
We define as the learning steps = 100, the numberof learning data sets = 200. Learning data contain 100
images with the object and 100 images without the object. Figure 3 shows the learning accuracy and step on18