WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents
arXiv:2605.20306v1 Announce Type: cross Abstract: We introduce WildRoadBench, a wild aerial road-damage grounding benchmark that couples direct visual grounding by vision-language models with autonomous research-and-engineering by LLM-driven agents on a single professionally annotated UAV corpus. The same image set and the same per-class AP_50 metric are evaluated under two protocols. The VLM Track measures whether a fixed VLM can localise domain-specific damage from one image and one short prompt under a unified prompting, decoding and parsing pipeline. The Agent Track measures whether an aut
The proliferation of advanced vision-language models and the increasing sophistication of LLM-driven agents are enabling new applications in real-world infrastructure assessment and maintenance.
This benchmark signifies a tangible step towards autonomous systems capable of real-time infrastructure inspection and proactive maintenance, potentially saving significant costs and improving safety.
The ability to couple visual grounding models with autonomous agents for specialized, real-world tasks like road-damage detection marks an evolution from general-purpose AI to highly applied, domain-specific autonomous operations.
- · Construction and maintenance firms
- · Autonomous drone manufacturers
- · AI model developers
- · Infrastructure management platforms
- · Traditional manual inspection services
- · Legacy infrastructure assessment software
Autonomous agents will become increasingly proficient at identifying and categorizing specific types of infrastructure damage.
The cost and time required for infrastructure inspection and maintenance will significantly decrease, leading to better maintained public works.
This capability could expand to autonomous quality control and compliance monitoring across a wide range of physical assets, reducing human oversight requirements.
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Read at arXiv cs.LG