Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images
Qifan Wu, Daqiang Feng, Changqing Cao, Xiaodong Zeng, Zhejun Feng, Jin Wu, Ziqiang Huang
Abstract
In recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. Our model can perform object recognition and segmentation in parallel. This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance segmentation effect. We construct WFA-1400 based on the DOTA dataset because of the shortage of remote sensing mask datasets. We compare the improved algorithm with other state-of-the-art algorithms. The object detection AP50 and AP increased by 1–2% and 1%, respectively, objectively proving the effectiveness and the feasibility of the improved model.