A Versatile Benchmark for Pavement Distress Perception and Interactive Vision Language Analysis NeurIPS 2024 Datasets and Benchmarks Track Paper Leaderboard Benchmark code PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision Language Analysis 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 image…
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