Vehicle Detection and Counting in High-Resolution Aerial Images Using Convolutional Regression Neural Network

Abstract

Vehicle detection and counting in aerial images have become an interesting research focus since the last decade. It is important for a wide range of applications, such as urban planning and traffic management. However, this task is a challenging one due to the small size of the vehicles, their different types and orientations, and similarity in their visual appearance, and some other objects, such as air conditioning units on buildings, trash bins, and road marks. Many methods have been introduced in the literature for solving this problem. These methods are either based on shallow learning or deep learning approaches. However, these methods suffer from relatively low precision and recall rate. This paper introduces an automated vehicle detection and counting system in aerial images. The proposed system utilizes convolution neural network to regress a vehicle spatial density map across the aerial image. It has been evaluated on two publicly available data sets, namely, Munich and Overhead Imagery Research Data Set. The experimental results show that our proposed system is efficient and effective, and produces higher precision and recall rate than the comparative methods.