Fully Convolutional SVM for Car Detection In Uav Imagery

Abstract

Semantic segmentation is understanding images at pixel level, which becomes increasingly vital in unmanned aerial vehicles (UAV) imagery classification tasks. With the powerful calculating ability of GPU, a growing number of deep convolutional neural networks (DCNNs) are designed to address semantic segmentation challenges. However, compared with CPU, GPU is much more costly, and GPU relies on powerful supplementary equipments to support it. Therefore, GPU-based methods are hard to carry out in practical applications. In this study, we propose a CPU-based method named fully convolutional support vector machine (FCSVM) to address the semantic segmentation challenge in UAV images. On the one hand, we adopt the SVM kernel from the original convolutional SVM networks (CSVM), which completes classification at image level. On the other hand, FCSVM consists of two processes which are a compressed process and a extensive process. In the compressed process, the FCSVM has convolutional layers and reduction layers. In the extensive process, the FCSVM has convolutional layers and upsampling layers. This structure allows FCSVM to classify images at pixel level using limited number of training data. The experiments are implemented on one UAV dataset with very little training data. The result shows our FCSVM achieves competitive performance compared to modern state-of-the-art semantic segmentation methods.