SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection
CoRR(2024)
摘要
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.
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