FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection

TL;DR

This paper introduces FPMT, a semi-supervised traffic incident detection model that leverages GAN-based data augmentation and probabilistic pseudo-mixing to improve accuracy with limited labeled data.

Contribution

It presents a novel semi-supervised learning framework combining GANs and mix-up techniques within MixText for traffic incident detection.

Findings

Outperforms existing methods on four real datasets

Maintains high accuracy with low label rates

Effective data augmentation improves model robustness

Abstract

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.