Nichovlasy/BG-MoENet

This is a 6-day-old, 0-star repository with no forks or community adoption. The README describes a specific deep learning architecture combining boundary guidance with mixture-of-experts routing for a domain-specific task (power line segmentation from UAV imagery). While the technical combination is novel—applying MoE with boundary constraints to this problem—the implementation appears to be a research prototype or academic paper implementation with no production signals, user base, or ecosystem. The work is likely from a paper submission or thesis project. Frontier risk is medium because: (1) this is a specialized vertical application (power line detection) rather than general infrastructure, but (2) frontier labs have shown interest in computer vision for critical infrastructure, and (3) adding boundary-guided MoE routing to an existing segmentation model is within their capability scope if the use case becomes strategically relevant. The project has zero momentum, no alternate implementations to displace, and no community dependency graph. It would be trivially reproducible by a competent computer vision team once the paper is published. Defensibility is low due to age, zero adoption, and research-paper-stage maturity.