SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection
<|reference_start|>SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection: Feature-fusion networks with duplex encoders have proven to be an effective technique to solve the freespace detection problem. However, despite the compelling results achieved by previous research efforts, the exploration of adequate and discriminative heterogeneous feature fusion, as well as the development of fallibility-aware loss functions remains relatively scarce. This paper makes several significant contributions to address these limitations: (1) It presents a novel heterogeneous feature fusion block, comprising a holistic attention module, a heterogeneous feature contrast descriptor, and an affinity-weighted feature recalibrator, enabling a more in-depth exploitation of the inherent characteristics of the extracted features, (2) it incorporates both inter-scale and intra-scale skip connections into the decoder architecture while eliminating redundant ones, leading to both improved accuracy and computational efficiency, and (3) it introduces two fallibility-aware loss functions that separately focus on semantic-transition and depth-inconsistent regions, collectively contributing to greater supervision during model training. Our proposed heterogeneous feature fusion network (SNE-RoadSegV2), which incorporates all these innovative components, demonstrates superior performance in comparison to all other freespace detection algorithms across multiple public datasets. Notably, it ranks the 1st on the official KITTI Road benchmark.<|reference_end|>
@article{feng2024sne-roadsegv2:,
    title={SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility
  Awareness for Freespace Detection},
    author={Yi Feng, Yu Ma, Qijun Chen, Ioannis Pitas, Rui Fan},
    journal={arXiv preprint arXiv:2402.18918},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18918},
    primaryClass={cs.CV}
}