  Object detection has been one of the most active topics in computer vision
for the past years. Recent works have mainly focused on pushing the
state-of-the-art in the general-purpose COCO benchmark. However, the use of
such detection frameworks in specific applications such as autonomous driving
is yet an area to be addressed. This study presents an enhanced 2D object
detector based on Faster R-CNN that is better suited for the context of
autonomous vehicles. Two main aspects are improved: the anchor generation
procedure and the performance drop in minority classes. The default uniform
anchor configuration is not suitable in this scenario due to the perspective
projection of the vehicle cameras. Therefore, we propose a perspective-aware
methodology that divides the image into key regions via clustering and uses
evolutionary algorithms to optimize the base anchors for each of them.
Furthermore, we add a module that enhances the precision of the second-stage
header network by including the spatial information of the candidate regions
proposed in the first stage. We also explore different re-weighting strategies
to address the foreground-foreground class imbalance, showing that the use of a
reduced version of focal loss can significantly improve the detection of
difficult and underrepresented objects in two-stage detectors. Finally, we
design an ensemble model to combine the strengths of the different learning
strategies. Our proposal is evaluated with the Waymo Open Dataset, which is the
most extensive and diverse up to date. The results demonstrate an average
accuracy improvement of 6.13% mAP when using the best single model, and of
9.69% mAP with the ensemble. The proposed modifications over the Faster R-CNN
do not increase computational cost and can easily be extended to optimize other
anchor-based detection frameworks.
