用无标注数据训练模型，少标数据也能精准识别车位。
An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data
- 用自编码器从无标签数据学视觉特征，再用少量标注数据分类。
- 跨数据集测试下准确率93%~96%，大幅降低标注需求。
- 多模型集成提升鲁棒性，适合标注少、环境变化大的场景。
车位分类是智能交通系统的基础任务，但现有深度学习方法依赖大量标注数据，且在异构环境下的泛化能力有限。为解决这一问题，本文提出一种基于卷积自编码器的自教学习框架，利用无监督表示学习从无标注数据中提取可迁移的视觉特征，并将训练好的编码器作为固定特征提取器，在目标域仅使用少量标注样本进行有监督分类。为进一步增强鲁棒性并缓解模型架构偏差，采用异构自编码器的集成结构，每个分支配备独立分类头，推理时融合预测结果。在PKLot和CNRPark基准上进行跨数据集评估，实验表明该方法显著减少标注需求，同时在显著领域偏移下仍保持良好性能，数据受限场景下准确率达到93%至96%。
原文摘要 · Abstract (English)
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.
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