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<p>Electric vehicles in leisure facilities pose significant safety concerns, demanding robust autonomous driving systems with precise visual perception algorithms. This paper introduces a novel knowledge distillation framework, relational alignment distillation (RA-Distill), for semantic segmentation of country club environments. The proposed method addresses critical challenges for achieving reliable accuracy in complex environments while ensuring computational efficiency for deployment on resource-limited hardware. RA-Distill extracts rich relational knowledge by computing Gram matrices from channel attention maps to analyze inter-channel correlations and global contexts. This structural information is then transferred from a complex teacher network to a lightweight student network using a similarity metric based on the centered kernel alignment for ensuring the invariance to scaling and orthogonal transformations. Experiments were conducted on a real-world country club dataset and the public CamVid dataset. The experimental results demonstrate that the proposed RA-Distill significantly outperforms previous distillation methods. Our lightweight student model surpasses the performance of the teacher network in the country club environments, enhancing the reliability of the collision avoidance system.</p>

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RA-Distill: Relational Alignment Distillation Based on Gram Matrix for Semantic Segmentation of Country Club Environments

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Yunseok Yang,
  • Sang Jun Lee

摘要

Electric vehicles in leisure facilities pose significant safety concerns, demanding robust autonomous driving systems with precise visual perception algorithms. This paper introduces a novel knowledge distillation framework, relational alignment distillation (RA-Distill), for semantic segmentation of country club environments. The proposed method addresses critical challenges for achieving reliable accuracy in complex environments while ensuring computational efficiency for deployment on resource-limited hardware. RA-Distill extracts rich relational knowledge by computing Gram matrices from channel attention maps to analyze inter-channel correlations and global contexts. This structural information is then transferred from a complex teacher network to a lightweight student network using a similarity metric based on the centered kernel alignment for ensuring the invariance to scaling and orthogonal transformations. Experiments were conducted on a real-world country club dataset and the public CamVid dataset. The experimental results demonstrate that the proposed RA-Distill significantly outperforms previous distillation methods. Our lightweight student model surpasses the performance of the teacher network in the country club environments, enhancing the reliability of the collision avoidance system.