Advanced Camera Image Cropping Approach for CNN-Based End-to-End Controls on Sustainable Computing

Yunsick Sung, Yong Jin, Jeonghoon Kwak, Sang-Geol Lee and Kyungeun Cho
Additional contact information
Yunsick Sung: Department of Multimedia Enginnering, Dongguk University-Seoul, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Yong Jin: Department of Multimedia Enginnering, Dongguk University-Seoul, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Jeonghoon Kwak: Department of Multimedia Enginnering, Dongguk University-Seoul, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Sang-Geol Lee: Department of Electrical and Computer Engineering, Pusan National University, 2 Busandaehak-ro, 63 Beon-gil, Geumjeong-gu, Busan 46241, Korea
Kyungeun Cho: Department of Multimedia Enginnering, Dongguk University-Seoul, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea

Sustainability, 2018, vol. 10, issue 3, 1-13

Abstract: Recent research on deep learning has been applied to a diversity of fields. In particular, numerous studies have been conducted on self-driving vehicles using end-to-end approaches based on images captured by a single camera. End-to-end controls learn the output vectors of output devices directly from the input vectors of available input devices. In other words, an end-to-end approach learns not by analyzing the meaning of input vectors, but by extracting optimal output vectors based on input vectors. Generally, when end-to-end control is applied to self-driving vehicles, the steering wheel and pedals are controlled autonomously by learning from the images captured by a camera. However, high-resolution images captured from a car cannot be directly used as inputs to Convolutional Neural Networks (CNNs) owing to memory limitations; the image size needs to be efficiently reduced. Therefore, it is necessary to extract features from captured images automatically and to generate input images by merging the parts of the images that contain the extracted features. This paper proposes a learning method for end-to-end control that generates input images for CNNs by extracting road parts from input images, identifying the edges of the extracted road parts, and merging the parts of the images that contain the detected edges. In addition, a CNN model for end-to-end control is introduced. Experiments involving the Open Racing Car Simulator (TORCS), a sustainable computing environment for cars, confirmed the effectiveness of the proposed method for self-driving by comparing the accumulated difference in the angle of the steering wheel in the images generated by it with those of resized images containing the entire captured area and cropped images containing only a part of the captured area. The results showed that the proposed method reduced the accumulated difference by 0.839% and 0.850% compared to those yielded by the resized images and cropped images, respectively.

Keywords: self-driving; convolution neural network; end-to-end control (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/10/3/816/pdf (application/pdf)
https://www.mdpi.com/2071-1050/10/3/816/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:10:y:2018:i:3:p:816-:d:136370

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().