Papers › F2DNet: Fast Focal Detection Network for Pedestrian Detection
F2DNet: Fast Focal Detection Network for Pedestrian Detection
Abdul Hannan Khan, Mohsin Munir, Ludger van Elst, Andreas Dengel
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Pedestrian Detection | Caltech | F2DNet (extra data) | Heavy MR^-2 | 20.42 | #2 of 33 | Archive leaderboard | report |
| Pedestrian Detection | Caltech | F2DNet (extra data) | Reasonable Miss Rate | 1.71 | #2 of 33 | Archive leaderboard | report |
| Pedestrian Detection | Caltech | F2DNet | Heavy MR^-2 | 38.7 | #4 of 33 | Archive leaderboard | report |
| Pedestrian Detection | Caltech | F2DNet | Reasonable Miss Rate | 2.2 | #4 of 33 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | F2DNet (extra data) | Heavy MR^-2 | 26.23 | #4 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | F2DNet (extra data) | Reasonable MR^-2 | 7.8 | #4 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | F2DNet (extra data) | Small MR^-2 | 9.43 | #4 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | F2DNet (extra data) | Test Time | 0.44s/img | #4 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | F2DNet | Heavy MR^-2 | 32.6 | #6 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | F2DNet | Reasonable MR^-2 | 8.7 | #6 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | F2DNet | Small MR^-2 | 11.3 | #6 of 22 | Archive leaderboard | report |
| Pedestrian Detection | CityPersons | F2DNet | Test Time | 0.44s/img | #6 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: F2DNet
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