Camera-Based Blind Spot Detection with a General Purpose Lightweight Neural Network

Yiming Zhao,Lin Bai,Yecheng Lyu,Xinming Huang

doi:10.3390/electronics8020233

Abstract

Blind spot detection is an important feature of Advanced Driver Assistance Systems (ADAS). In this paper, we provide a camera-based deep learning method that accurately detects other vehicles in the blind spot, replacing the traditional higher cost solution using radars. The recent breakthrough of deep learning algorithms shows extraordinary performance when applied to many computer vision tasks. Many new convolutional neural network (CNN) structures have been proposed and most of the networks are very deep in order to achieve the state-of-art performance when evaluated with benchmarks. However, blind spot detection, as a real-time embedded system application, requires high speed processing and low computational complexity. Hereby, we propose a novel method that transfers blind spot detection to an image classification task. Subsequently, a series of experiments are conducted to design an efficient neural network by comparing some of the latest deep learning models. Furthermore, we create a dataset with more than 10,000 labeled images using the blind spot view camera mounted on a test vehicle. Finally, we train the proposed deep learning model and evaluate its performance on the dataset.

Highlights

In the 2012 ILSVRC competition, deep convolutional neural network designed by Hinton et al
The question is—do we really need such a large network to solve a specific real-world problem? If the task is not that complex and we ought to avoid the very deep structure, how should we adapt the deep learning models? Do we still need residual learning? In order to answer those questions, we propose a network structure based on AlexNet [1]
We discuss how to design a neural network with only a few layers for real-time embedded applications, such as blind spot detection

Summary

Introduction

In the 2012 ILSVRC competition, deep convolutional neural network designed by Hinton et al.achieved the lowest error rate of 15.3% that is 10.8% better than the runner up [1]. In the 2012 ILSVRC competition, deep convolutional neural network designed by Hinton et al. The large and complex classification dataset in ILSVRC with 1000 object categories is widely regarded as a benchmark to evaluate different machine learning models [2]. This milestone achievement attracted many researchers to the field of deep neural networks. Adaption of new neural network structures continually pushed the error rate lower. In ILSVRC-2014, GoogLeNet achieved 6.67% error rate using inception module [3]. In ILSVRC-2015, the invention of residual error in ResNet made it possible to train a very deep network with more than 100 convolution layers [7].

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Conclusion