Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization
arXiv:2510.10028v2 Announce Type: replace Abstract: The rapid advancement of Low-Altitude Economy Networks (LAENets) has enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection. To support these scenarios, unmanned aerial vehicles (UAVs) equipped with onboard vision-language models (VLMs) offer a promising solution for real-time multimodal inference. However, ensuring both inference accuracy and communication efficiency remains a significant challenge due to limited onboard resources and dynamic network conditions. In this paper, we
The rapid advancement of AI models and the proliferation of UAVs are converging, making efficient onboard inference critical for immediate, real-world applications.
This development addresses a key bottleneck for deploying autonomous AI systems in low-altitude operational zones, impacting defense, logistics, and surveillance capabilities.
The ability to perform robust vision-language inference directly on UAVs, even with limited resources, significantly expands their operational autonomy and utility.
- · UAV manufacturers
- · AI model developers
- · Defense contractors
- · Logistics and delivery services
- · Ground-based surveillance systems (in specific contexts)
- · Legacy aerospace companies (slow to adapt)
- · Companies reliant on centralized, high-latency processing
Increased real-time data collection and analysis capabilities from low-altitude environments.
Enhanced operational autonomy and decision-making for drone fleets, reducing human oversight requirements.
The proliferation of AI-enabled drone networks could lead to new forms of urban monitoring and autonomous service delivery, raising privacy and regulatory concerns.
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Read at arXiv cs.LG