  Two-stage detectors are state-of-the-art in object detection as well as
pedestrian detection. However, the current two-stage detectors are inefficient
as they do bounding box regression in multiple steps i.e. in region proposal
networks and bounding box heads. Also, the anchor-based region proposal
networks are computationally expensive to train. We propose F2DNet, a novel
two-stage detection architecture which eliminates redundancy of current
two-stage detectors by replacing the region proposal network with our focal
detection network and bounding box head with our fast suppression head. We
benchmark F2DNet on top pedestrian detection datasets, thoroughly compare it
against the existing state-of-the-art detectors and conduct cross dataset
evaluation to test the generalizability of our model to unseen data. Our F2DNet
achieves 8.7\%, 2.2\%, and 6.1\% MR-2 on City Persons, Caltech Pedestrian, and
Euro City Person datasets respectively when trained on a single dataset and
reaches 20.4\% and 26.2\% MR-2 in heavy occlusion setting of Caltech Pedestrian
and City Persons datasets when using progressive fine-tunning. Furthermore,
F2DNet have significantly lesser inference time compared to the current
state-of-the-art. Code and trained models will be available at
https://github.com/AbdulHannanKhan/F2DNet.
