| CNN-BASED OBJECT DETECTION AND DISTANCE PREDICTION FOR
AUTONOMOUS DRIVING USING STEREO IMAGES |
| Jin Gyu Song , Joon Woong Lee |
| Department of Industrial Engineering, Chonnam National University |
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| ABSTRACT |
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Convolutional neural networks (CNNs) have been successful for tasks such as object detection; however,
they involve time-consuming processes. Therefore, there are difficulties in applying these CNNs to autonomous driving.
Moreover, most autonomous driving technologies require both object detection and distance prediction. However, CNNs
that predict distance involve more time-consuming processes than object detection models. In addition, the applications
for autonomous driving require object detection and distance prediction accuracy. This paper proposes an end-to-end
trainable CNN that can meet these requirements. The proposed CNN accurately implements object detection and
distance prediction in real time using stereo images. We demonstrate the superiority of the proposed CNN using stereo
images from the KITTI 3D object detection dataset. |
| Key Words:
Autonomous driving, Object detection, Distance prediction, Real-time processing |
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Corresponding Author.
Joon Woong Lee , Email. 
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