Thermal Detection of People with Mobility Restrictions for Barrier Reduction at Traffic Lights Controlled Intersections

TL;DR

This paper introduces a thermal imaging-based traffic light system that improves accessibility for people with mobility restrictions, addressing privacy and environmental challenges with a new detection architecture and dataset.

Contribution

It presents YOLO-Thermal, a novel thermal detector architecture, and the TD4PWMR dataset, advancing detection accuracy and robustness for barrier-free intersections.

Findings

Thermal detector outperforms existing methods in accuracy.

System effectively enhances barrier-free intersection accessibility.

Proposed approach maintains robustness under adverse conditions.

Abstract

Rapid advances in deep learning for computer vision have driven the adoption of RGB camera-based adaptive traffic light systems to improve traffic safety and pedestrian comfort. However, these systems often overlook the needs of people with mobility restrictions. Moreover, the use of RGB cameras presents significant challenges, including limited detection performance under adverse weather or low-visibility conditions, as well as heightened privacy concerns. To address these issues, we propose a fully automated, thermal detector-based traffic light system that dynamically adjusts signal durations for individuals with walking impairments or mobility burden and triggers the auditory signal for visually impaired individuals, thereby advancing towards barrier-free intersection for all users. To this end, we build the thermal dataset for people with mobility restrictions (TD4PWMR), designed to capture diverse pedestrian scenarios, particularly focusing on individuals with mobility aids or mobility burden under varying environmental conditions, such as different lighting, weather, and crowded urban settings. While thermal imaging offers advantages in terms of privacy and robustness to adverse conditions, it also introduces inherent hurdles for object detection due to its lack of color and fine texture details and generally lower resolution of thermal images. To overcome these limitations, we develop YOLO-Thermal, a novel variant of the YOLO architecture that integrates advanced feature extraction and attention mechanisms for enhanced detection accuracy and robustness in thermal imaging. Experiments demonstrate that the proposed thermal detector outperforms existing detectors, while the proposed traffic light system effectively enhances barrier-free intersection. The source codes and dataset are available at https://github.com/leon2014dresden/YOLO-THERMAL.