模式衍生视觉群体游戏：用于拦截与可持续性审计的多尺度无人机视觉状态
Pattern-Derived Visual Swarm Games: Multi-Scale Drone-Vision States for Interception and Sustainability Audits
- Bahçeşehir University（巴切谢希尔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究将无人机视觉标注流转为虚拟群体游戏状态，通过Bloom表示、多尺度有限博弈及相关技术提升屏幕安全与目标定位准确率，选出预算自适应策略用于拦截与可持续性审计。
AI中文摘要：
我们在不控制物理无人机的情况下，将无人机视觉标注流转换为虚拟群体游戏状态。将VisDrone和UAVSwarm元数据压缩为Bloom表示；确定性探测生成有界能力向量、图像空间编队、有限零和收益以及人类可读的视觉叠加层。审计规模从6×6扩展至32×32有限博弈，并添加包含库存、疲劳、适应、暴露、压力、预算、数据增长、模型改进和熵预算状态变量的重复马尔可夫层。局部屏幕调优将鲁棒屏幕安全性从0.526提升至0.593，调优后的32×32屏幕达到0.616的价值。现场读数审计显示固定像素光栅未单调提升：128×128准确率为67.2%，热点误差为0.136。诊断误差为图像平面带宽收缩。基于尺度归一化高斯带宽的有限经验风险编码器选择λ=1.50的尺度归一化编码器，在128×128时达到77.6%的准确率，联合损失降低0.185。服务器端审计检查16777216个目标定位状态，对16777216条轨迹进行的32轮重复博弈审计选择价值为0.461的预算自适应策略。
英文摘要：
We convert drone-vision annotation streams into virtual swarm-game states without controlling physical drones. VisDrone and UAVSwarm metadata are compressed into a Bloom representation; deterministic probes produce bounded capability vectors, image-space formations, finite zero-sum payoffs, and human-readable visual overlays. The audit scales from $6\times 6$ to $32\times 32$ finite games and adds a repeated Markov layer with stock, fatigue, adaptation, exposure, stress, budget, data-growth, model-improvement, and entropy-budget state variables. Local screen tuning raises robust screen security from $0.526$ to $0.593$, and the $32\times 32$ tuned screen reaches value $0.616$. A field readout audit shows that fixed-pixel rasters do not improve monotonically: $128\times 128$ accuracy is $67.2\%$ and hotspot error is $0.136$. The diagnosed error is shrinking image-plane bandwidth. A finite empirical-risk encoder over scale-normalized Gaussian bandwidths selects a scale-normalized encoder with $λ=1.50$, reaching $77.6\%$ accuracy at $128\times 128$ and reducing joint loss by $0.185$. A server-side audit checks $16{,}777{,}216$ target-localization states, and a 32-round repeated-game audit over $16{,}777{,}216$ trajectories selects a budget-adaptive policy with value $0.461$.
