Remote sensing aircraft detection method based on lightweight YOLOv4

Remote sensing aircraft target detection is an important task in the field of remote sensing images interpretation. The general target detection network often includes a large number of parameters, slow detection speed, and poor performance when directly applied to aircraft detection tasks. In order to solve the issues, a novel remote sensing aircraft target detection method based on lightweight YOLOv4 is proposed in this paper. Firstly, Lightweight YOLOv4 adopts the MobileNetV3 and depthwise separable convolution to greatly reduce the amount of model parameters. Then, to further enhance the feature extraction ability of the network and make the network more lightweight, this paper uses the feature enhancement module (FEM) and residual fusion module (RFM). Extensive experiments on the DOTA aircraft dataset demonstrate that the Lightweight YOLOv4 can significantly improve the detection accuracy and efficiency, as well as owns fewer model parameters.