基于集成的自监督学习方法用于数据有限场景下的停车位分类

中文摘要 AI 辅助

停车位分类是智能交通系统中的基础任务，但大多数深度学习方法依赖大量标注数据，且在异构环境中的泛化能力有限。为解决这些局限，本文研究一种基于卷积自编码器无监督表示学习的自监督学习框架。该方法从未标注数据中学习可迁移的视觉表示，并将学习到的编码器作为固定特征提取器，用于目标域中有限标注样本的监督分类。为进一步增强鲁棒性并缓解架构偏差，采用异构自编码器的集成，搭配独立分类器头，推理时进行预测融合。在PKLot和CNRPark基准上开展跨数据集评估实验，结果表明，所提基于集成的策略大幅降低了标注需求，同时在显著域偏移下提升了鲁棒性，在数据受限场景中实现93%至96%的准确率。

英文摘要

Parking spot classification is a fundamental task in intelligent transportation systems, yet most deep learning approaches rely on large amounts of annotated data and exhibit limited generalization across heterogeneous environments. To address these limitations, we investigate a self-taught learning framework based on unsupervised representation learning with convolutional autoencoders. The proposed approach learns transferable visual representations from unlabeled data and reuses the learned encoders as fixed feature extractors for supervised classification with limited annotated samples in the target domain. To further enhance robustness and mitigate architectural bias, an ensemble of heterogeneous autoencoders is employed, with independent classifier heads and prediction fusion at inference time. Experiments conducted on the PKLot and CNRPark benchmarks under cross-dataset evaluation protocols show that the proposed ensemble-based strategy substantially reduces annotation requirements while improving robustness under significant domain shifts, achieving accuracies between 93\% and 96\% in data-constrained scenarios.

发表机构

• Pontifícia Universidade Católica do Paraná（巴拉那天主教大学）
• Universidade Federal do Paraná（巴拉那联邦大学）

机构由 AI 辅助整理，请以论文原文为准。