ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones

TL;DR

This paper introduces the ROADWork dataset for recognizing and navigating through work zones, demonstrating that fine-tuning models significantly enhances perception, detection, and navigation capabilities in these challenging scenarios.

Contribution

The paper presents a new dataset and benchmark for work zone perception, showing that fine-tuning models and simple techniques greatly improve recognition and navigation in work zones.

Findings

Fine-tuning models on ROADWork improves perception accuracy (+32.5% precision).

Video label propagation enhances instance segmentation (+2.6 AP).

Composing detectors and text spotters improves sign reading (+14.2% NED).

Abstract

Perceiving and autonomously navigating through work zones is a challenging and underexplored problem. Open datasets for this long-tailed scenario are scarce. We propose the ROADWork dataset to learn to recognize, observe, analyze, and drive through work zones. State-of-the-art foundation models fail when applied to work zones. Fine-tuning models on our dataset significantly improves perception and navigation in work zones. With ROADWork dataset, we discover new work zone images with higher precision (+32.5%) at a much higher rate (12.8$\times$) around the world. Open-vocabulary methods fail too, whereas fine-tuned detectors improve performance (+32.2 AP). Vision-Language Models (VLMs) struggle to describe work zones, but fine-tuning substantially improves performance (+36.7 SPICE). Beyond fine-tuning, we show the value of simple techniques. Video label propagation provides additional gains (+2.6 AP) for instance segmentation. While reading work zone signs, composing a detector and text spotter via crop-scaling improves performance +14.2% 1-NED). Composing work zone detections to provide context further reduces hallucinations (+3.9 SPICE) in VLMs. We predict navigational goals and compute drivable paths from work zone videos. Incorporating road work semantics ensures 53.6% goals have angular error (AE) < 0.5 (+9.9 %) and 75.3% pathways have AE < 0.5 (+8.1 %).