Faster RER-CNN: application to the detection of vehicles in aerial images

TL;DR

This paper enhances Faster R-CNN to better detect small, rotated vehicles in aerial images by incorporating rotation equivariance, achieving state-of-the-art results on challenging datasets.

Contribution

It introduces Faster RER-CNN, a novel extension of Faster R-CNN that effectively handles rotation invariance in aerial image object detection.

Findings

Achieves state-of-the-art results on VeDAI dataset.

Outperforms baseline Faster R-CNN on Munich and GoogleEarth datasets.

Effectively detects small, rotated vehicles in aerial imagery.

Abstract

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 .