PaveBench: 一种多功能的路面病害感知及交互视觉-语言分析基准
PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis
AI总结：
PaveBench针对路面状况评估中的视觉识别与多模态交互需求，提供分类、检测、分割及视觉-语言问答等四项任务，支持多轮交互与事实推理，填补现有数据集在多模态分析与实际应用连接上的空白。
摘要：
路面状况评估对道路安全和维护至关重要。现有研究在传统计算机视觉任务如分类、检测和分割上取得了显著进展。然而，实际应用中路面检查需要超越视觉识别，还需定量分析、解释和交互决策支持。现有数据集局限于单模态感知，缺乏多轮交互和事实推理支持，且未将感知与视觉-语言分析连接起来。为解决这些限制，我们引入PaveBench，一个大规模的路面病害感知及交互视觉-语言分析基准，基于真实高速公路检查图像。PaveBench支持四项核心任务：分类、目标检测、语义分割和视觉-语言问答。它提供统一的任务定义和评估协议。在视觉方面，PaveBench提供大规模注释，并包含一个经过精心挑选的干扰子集用于鲁棒性评估。它包含大量真实世界路面图像。在多模态方面，我们引入PaveVQA，一个支持单轮、多轮和专家校正交互的真实图像问答数据集，涵盖识别、定位、定量估计和维护推理。我们评估了几种最先进的方法并提供详细分析。我们还提出了一种简单有效的代理增强视觉问答框架，整合了领域特定模型作为工具与视觉-语言模型协同工作。该数据集可在：https://huggingface.co/datasets/MML-Group/PaveBench获取。
英文摘要：
Pavement condition assessment is essential for road safety and maintenance. Existing research has made significant progress. However, most studies focus on conventional computer vision tasks such as classification, detection, and segmentation. In real-world applications, pavement inspection requires more than visual recognition. It also requires quantitative analysis, explanation, and interactive decision support. Current datasets are limited. They focus on unimodal perception. They lack support for multi-turn interaction and fact-grounded reasoning. They also do not connect perception with vision-language analysis. To address these limitations, we introduce PaveBench, a large-scale benchmark for pavement distress perception and interactive vision-language analysis on real-world highway inspection images. PaveBench supports four core tasks: classification, object detection, semantic segmentation, and vision-language question answering. It provides unified task definitions and evaluation protocols. On the visual side, PaveBench provides large-scale annotations and includes a curated hard-distractor subset for robustness evaluation. It contains a large collection of real-world pavement images. On the multimodal side, we introduce PaveVQA, a real-image question answering (QA) dataset that supports single-turn, multi-turn, and expert-corrected interactions. It covers recognition, localization, quantitative estimation, and maintenance reasoning. We evaluate several state-of-the-art methods and provide a detailed analysis. We also present a simple and effective agent-augmented visual question answering framework that integrates domain-specific models as tools alongside vision-language models. The dataset is available at: https://huggingface.co/datasets/MML-Group/PaveBench.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
