SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection

Feature-fusion networks with duplex encoders have proved to be an effective technique for solving the road 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 article makes several significant contributions to address these limitations: 1) it presents a novel heterogeneous feature fusion block (HF2B), comprising a holistic attention module (HAM), a heterogeneous feature contrast descriptor (HFCD), and an affinity-weighted feature recalibrator (AWFR), enabling more in-depth exploitation of the inherent characteristics of the extracted features; 2) it incorporates both interscale and intrascale 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 SNE-RoadSegV2, which incorporates all these innovative components, demonstrates superior performance in comparison to all other free-space detection algorithms across multiple public datasets.