Object Detection in Aerial Images Based on Cascaded CNN
Wei Zhang, Jiaojie Li, Shengxiang Qi · 2018
Object detection in aerial images is widely used for military applications, such as reconnaissance, target surveillance, battle damage assessment, et al. However, the tasks are very challenging due to a lot of factors, such as illumination variance, scene complexity, and platform motion. To deal with these problems, a new cascaded convolutional neural network (CNN) model for object detection from airborne videos is proposed. The proposed framework adopts a cascaded structure with three levels of deep CNNs that predict objects in a coarse-to-fine manner. The experimental results showed that the proposed method can achieve better performance.