README.md

---
pretty_name: PaveBench
size_categories:
- 10K<n<100K
license: cc-by-nc-sa-4.0
tags:
- computer-vision
- vision-language
- visual-question-answering
- image-segmentation
- object-detection
- image-classification
- multimodal-learning
- benchmark
task_categories:
- question-answering
- image-segmentation
- object-detection
- image-classification
language:
- en
---

# PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis

![PaveBench Overview](figures/PaveBench.png)

📍 Data Availability: The dataset will be publicly released under the CC BY-NC-SA 4.0 license upon official acceptance of the associated paper.

Stay tuned for updates — the Hugging Face repository will be updated with download links and detailed documentation once the paper is accepted.


## Abstract

PaveBench is a large-scale benchmark for pavement distress perception and interactive vision-language analysis on real-world highway inspection images. It supports four core tasks: classification, object detection, semantic segmentation, and vision-language question answering. On the visual side, PaveBench provides large-scale annotations on real top-down pavement images and includes a curated hard-distractor subset for robustness evaluation. On the multimodal side, it introduces PaveVQA, a real-image question answering dataset supporting single-turn, multi-turn, and expert-corrected interactions, covering recognition, localization, quantitative estimation, and maintenance reasoning.



## About the Dataset

PaveBench is built on real-world highway inspection images collected in Liaoning Province, China, using a highway inspection vehicle equipped with a high-resolution line-scan camera. The captured images are top-down orthographic pavement views, which preserve the geometric properties of distress patterns and support reliable downstream quantification. The dataset provides unified annotations for multiple pavement distress tasks and is designed to connect visual perception with interactive vision-language analysis.

The visual subset contains **20,124** high-resolution pavement images of size **512 × 512**. It supports:
- image classification
- object detection
- semantic segmentation

In addition, the multimodal subset, **PaveVQA**, contains **32,160** question-answer pairs, including:
- **10,050** single-turn queries
- **20,100** multi-turn interactions
- **2,010** error-correction pairs

These question-answer pairs cover recognition, localization, quantitative estimation, severity assessment, and maintenance recommendation. 
## Distress Categories

PaveBench includes six visual categories:
- Longitudinal Crack
- Transverse Crack
- Alligator Crack
- Patch
- Pothole
- Negative Sample

These annotations are organized through a hierarchical pipeline covering classification, detection, and segmentation. 

## Hard Distractors

A key feature of PaveBench is its curated **hard-distractor subset**. During annotation, the dataset explicitly retains visually confusing real-world patterns such as:
- pavement stains
- shadows
- road markings

These distractors often co-occur with real pavement distress and closely resemble true distress patterns, making the benchmark more realistic and more challenging for robustness evaluation. 

## PaveVQA

PaveVQA is a real-image visual question answering benchmark built on top of PaveBench. It supports:
- single-turn QA
- multi-turn dialogue
- expert-corrected interactions

The questions are designed around practical pavement inspection needs, including:
- presence verification
- distress classification
- localization
- quantitative analysis
- severity assessment
- maintenance recommendation

Structured metadata derived from visual annotations, such as bounding boxes, pixel area, and skeleton length, is used to support grounded and low-hallucination question answering.

## Dataset Statistics

According to the paper:
- Visual subset: **20,124** images
- Image resolution: **512 × 512**
- VQA subset: **32,160** QA pairs
- Four primary analysis tasks
- Fourteen fine-grained VQA sub-categories

PaveBench is designed to provide a unified foundation for both precise visual perception and interactive multimodal reasoning in the pavement domain. 

## Benchmark Tasks

PaveBench supports four core tasks:
1. Classification
2. Object Detection
3. Semantic Segmentation
4. Vision-Language Question Answering

It also includes an agent-augmented evaluation setting where vision-language models are combined with domain-specific tools for more reliable quantitative analysis. 

## Citation
If you use this dataset in your work, please cite it as:
```bibtex
@article{li2026pavebench,
  title={PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis},
  author={Li, Dexiang and Che, Zhenning and Zhang, Haijun and Zhou, Dongliang and Zhang, Zhao and Han, Yahong},
  journal={arXiv preprint arXiv:2604.02804},
  year={2026},
  url={https://arxiv.org/abs/2604.02804}
}
```

---
license: cc-by-nc-sa-4.0
---