Vehicle Detection in Deep Learning

Abstract

Computer vision is developing rapidly with the support of deep learning techniques. This thesis proposes an advanced vehicle-detection model based on an improvement to classical convolutional neural networks. The advanced model was applied against a vehicle detection benchmark and was built to detect on-road objects. First, we propose a high-level architecture for our advanced model, which utilizes different state-of-the-art deep learning techniques. Then, we utilize the residual neural networks and region proposal network to achieve competitive performance according to the vehicle detection benchmark. Lastly, we describe the developing trend of vehicle detection techniques and the future direction of research.

References in corpus (5)

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• Deep Learning in Neural Networks: An Overview
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• An Empirical Evaluation of Deep Learning on Highway Driving
• SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection