Papers › HybridNets: End-to-End Perception Network
HybridNets: End-to-End Perception Network
Dat Vu, Bao Ngo, Hung Phan
End-to-end Network has become increasingly important in multi-tasking. One prominent example of this is the growing significance of a driving perception system in autonomous driving. This paper systematically studies an end-to-end perception network for multi-tasking and proposes several key optimizations to improve accuracy. First, the paper proposes efficient segmentation head and box/class prediction networks based on weighted bidirectional feature network. Second, the paper proposes automatically customized anchor for each level in the weighted bidirectional feature network. Third, the paper proposes an efficient training loss function and training strategy to balance and optimize network. Based on these optimizations, we have developed an end-to-end perception network to perform multi-tasking, including traffic object detection, drivable area segmentation and lane detection simultaneously, called HybridNets, which achieves better accuracy than prior art. In particular, HybridNets achieves 77.3 mean Average Precision on Berkeley DeepDrive Dataset, outperforms lane detection with 31.6 mean Intersection Over Union with 12.83 million parameters and 15.6 billion floating-point operations. In addition, it can perform visual perception tasks in real-time and thus is a practical and accurate solution to the multi-tasking problem. Code is available at https://github.com/datvuthanh/HybridNets.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Drivable Area Detection | BDD100K val | HybridNets | Params (M) | 12.8 | #9 of 10 | Archive leaderboard | report |
| Drivable Area Detection | BDD100K val | HybridNets | mIoU | 90.5 | #9 of 10 | Archive leaderboard | report |
| Lane Detection | BDD100K val | HybridNets | Accuracy (%) | 85.4 | #3 of 11 | Archive leaderboard | report |
| Lane Detection | BDD100K val | HybridNets | IoU (%) | 31.6 | #3 of 11 | Archive leaderboard | report |
| Lane Detection | BDD100K val | HybridNets | Params (M) | 12.8 | #3 of 11 | 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
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