Vehicle Detection in Deep Learning

TL;DR

This paper presents an improved deep learning-based vehicle detection model that leverages residual networks and region proposal networks to achieve competitive performance on on-road object detection benchmarks.

Contribution

The paper introduces a novel vehicle detection architecture combining residual neural networks and region proposal networks for enhanced accuracy.

Findings

Achieved competitive results on vehicle detection benchmarks.

Utilized advanced deep learning techniques for improved detection.

Provided insights into future trends in vehicle detection research.

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.