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BiFNet: Bidirectional Fusion Network for Road Segmentation.

Haoran Li, Yaran Chen, Qichao Zhang

    IEEE Transactions on Cybernetics
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    Summary
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    This study introduces a new bidirectional fusion network (BiFNet) for intelligent driving systems. BiFNet enhances road segmentation by effectively fusing camera images and LiDAR bird's eye views (BEVs), improving drivable area detection.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Autonomous Driving

    Background:

    • Multisensor fusion is crucial for intelligent driving systems, providing drivable area information.
    • Current image-space fusion methods suffer from perspective compression, degrading distant road segmentation performance.

    Purpose of the Study:

    • To propose a novel bidirectional fusion network (BiFNet) for improved road segmentation.
    • To address the limitations of existing fusion techniques in intelligent driving systems.

    Main Methods:

    • Developed a bidirectional fusion network (BiFNet) integrating camera images and LiDAR bird's eye views (BEVs).
    • Introduced a dense space transformation (DST) module for mutual conversion between image and BEV spaces.
    • Implemented a context-based feature fusion module for sensor information integration.

    Main Results:

    • Achieved competitive road segmentation results on the KITTI dataset.
    • Demonstrated improved performance in detecting distant road areas compared to traditional methods.

    Conclusions:

    • BiFNet effectively fuses image and BEV data for robust road segmentation.
    • The proposed method enhances drivable area detection in intelligent driving systems.