VEHICLE DETECTION USING DEEP LEARNING WITH DEFORMABLE CONVOLUTION

Aiming at accurately detect vehicles in high-resolution remote sensing images, this paper proposes a target detection framework combining region-based fully convolutional networks (R-FCN) and deformable convolution (DCN). The difficulty of vehicle detection is that its pixel range is small and difficult to detect, R-FCN calculates confidence scores pixel by pixel, and uses a confidence scoring map related to the number of categories and local parts of the target as the output of the network, which can make full use of the limited feature information of vehicles. As to the precision reduction caused by geometric deformation of vehicle images, the fixed structure of the convolution kernel is improved, and the convolution kernel of part of the convolution layers and region of interest (RoI) pooling layers in the network are deformable to make it adapt to the deformation of targets. Experiments show that the R-FCN equipped with deformable convolution and deformable RoI pooling has advantages in detection precision and detection time.