Design and Implementation of the Vehicular Camera System using Deep Neural Network Compression

Abstract

In recent years, there is a growing interest in advanced driver assistance systems, which can reduce the risk of accidents on various roads. Many vehicular technologies use camera information to collect roadside information. But research and development of image recognition in embedded environments is challenging. Therefore, there is a requirement for research to apply open source image recognition technology to embedded platform. Deep learning, a technology that has been on the spotlight recently, shows excellent performance in image recognition. However, deep learning has a problem that the network size and amount of computation are too large. In this paper, we design and implement a deep learning based object recognition system to recognize vehicles on the road. We recognize vehicles using Faster-RCNN, which has excellent object recognition capabilities. However, it is problematic to apply it to an embedded device due to the feature of deep learning. Therefore, we propose and evaluate the performance of object recognition with quantization and pruning. Through the evaluation, we will show that the proposed system reduces the network size to 16% and reduces the operating time to 64% without a significant decrease in recognition accuracy.