用光照扰动和权重平均提升越野场景分割精度
Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Boosting Off-Road Segmentation via Photometric Distortion and Exponential Moving Average
- 采用光照扭曲增强与权重指数平均提升泛化能力
- 仅用训练集即达88.8% mIoU，验证集表现优异
- 适合关注越野环境分割的自动驾驶研究者
我们报告了在非结构化越野环境的GOOSE 2D语义分割挑战中，应用高容量语义分割流水线的成果。采用FlashInternImage-B主干网络与UPerNet解码器，通过适应性调整成熟技术应对越野场景特性。训练策略结合强光度扭曲增强（模拟户外地形光照变化）与权重指数移动平均（EMA），以提升模型泛化能力。仅使用GOOSE训练数据集，便在验证集上达到88.8%的mIoU。
原文摘要 · Abstract (English)
We report on the application of a high-capacity semantic segmentation pipeline to the GOOSE 2D Semantic Segmentation Challenge for unstructured off-road environments. Using a FlashInternImage-B backbone together with a UPerNet decoder, we adapt established techniques, rather than designing new ones, to the distinctive conditions of off-road scenes. Our training recipe couples strong photometric distortion augmentation (to emulate the wide lighting variations of outdoor terrain) with an Exponential Moving Average (EMA) of weights for better generalization. Using only the GOOSE training dataset, we achieve 88.8\% mIoU on the validation set.
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