Faster RER-CNN: application to the detection of vehicles in aerial images
This work addresses the tedious task of vehicle detection in aerial imagery for photo-interpreters, though it is incremental as it builds on an existing method.
The paper tackled the problem of detecting small vehicles in aerial images by adapting Faster R-CNN to handle rotation equivariance, achieving state-of-the-art results on the VeDAI dataset and improvements on Munich and GoogleEarth datasets.
Detecting small vehicles in aerial images is a difficult job that can be challenging even for humans. Rotating objects, low resolution, small inter-class variability and very large images comprising complicated backgrounds render the work of photo-interpreters tedious and wearisome. Unfortunately even the best classical detection pipelines like Faster R-CNN cannot be used off-the-shelf with good results because they were built to process object centric images from day-to-day life with multi-scale vertical objects. In this work we build on the Faster R-CNN approach to turn it into a detection framework that deals appropriately with the rotation equivariance inherent to any aerial image task. This new pipeline (Faster Rotation Equivariant Regions CNN) gives, without any bells and whistles, state-of-the-art results on one of the most challenging aerial imagery datasets: VeDAI and give good results w.r.t. the baseline Faster R-CNN on two others: Munich and GoogleEarth .