  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.
