Improved Faster R-CNN With Multiscale Feature Fusion and Homography Augmentation for Vehicle Detection in Remote Sensing Images

Vehicle detection in remote sensing images has attracted remarkable attention for its important role in a variety of applications in traffic, security, and military fields. Motivated by the stunning success of region convolutional neural network (R-CNN) techniques, which have achieved the state-of-the-art performance in object detection task on benchmark data sets, we propose to improve the Faster R-CNN method with better feature extraction, multiscale feature fusion, and homography data augmentation to realize vehicle detection in remote sensing images. Extensive experiments on representative remote sensing data sets related to vehicle detection demonstrate that our method achieves better performance than the state-of-the-art approaches. The source code will be made available (after the review process).