For traffic incident detection, the acquisition of data and labels is notably resource-intensive, rendering semi-supervised traffic incident detection both a formidable and consequential challenge. Thus, this paper focuses on traffic incident detection with a semi-supervised learning way. It proposes a semi-supervised learning model named FPMT within the framework of MixText. The data augmentation module introduces Generative Adversarial Networks to balance and expand the dataset. During the mix-up process in the hidden space, it employs a probabilistic pseudo-mixing mechanism to enhance regularization and elevate model precision. In terms of training strategy, it initiates with unsupervised training on all data, followed by supervised fine-tuning on a subset of labeled data, and ultimately completing the goal of semi-supervised training. Through empirical validation on four authentic datasets, our FPMT model exhibits outstanding performance across various metrics. Particularly noteworthy is its robust performance even in scenarios with low label rates.

翻译：在交通事件检测中，数据和标签的获取成本高昂，这使得半监督交通事件检测成为一个极具挑战性且意义重大的课题。因此，本文聚焦于采用半监督学习方式进行交通事件检测。在MixText框架内，本文提出了一种名为FPMT的半监督学习模型。其数据增强模块引入生成对抗网络以平衡和扩展数据集。在隐藏空间的混合过程中，模型采用概率伪混合机制以增强正则化并提升模型精度。在训练策略方面，模型首先对所有数据进行无监督训练，随后在部分有标签数据上进行有监督微调，最终完成半监督训练的目标。通过在四个真实数据集上的实证验证，我们的FPMT模型在各项指标上均展现出卓越性能。尤其值得注意的是，即使在标签率较低的场景下，模型仍表现出鲁棒的性能。