Vehicle detection from high-resolution aerial images using spatial pyramid pooling-based deep convolutional neural networks

Abstract

In recent years, vehicle detection from aerial images obtained using unmanned aerial vehicles (UAVs) has become a research focus in image processing as remote sensing platforms on UAVs are rapidly popularised. This study proposes a detection algorithm using a deep convolutional neural network (DCNN) based on multi-scale spatial pyramid pooling (SPP). By using multi-scale SPP models to sample characteristic patterns with different sizes, feature vectors with a fixed length are generated. This avoids the stretching- or cropping-induced deformation of input images of different sizes, thus improving the detection effect. In addition, an imaging pre-processing algorithm based on maximum normed gradient (NG) with multiple thresholds is proposed. By using this algorithm, this research restores the edges of objects disturbed by clutter in the environment. Meanwhile, the raised candidate object extraction algorithm based on the maximum binarized NG entails fewer computations as it generates fewer candidate windows. Experimental results indicate that the multi-scale SPP based DCNN can better adapt to input images of different sizes to learn of the multi-scale characteristics of objects, thus further improving the detection effect.