While semi‐autonomous drones are increasingly used for road infrastructure inspection, their insufficient ability to independently handle complex scenarios beyond initial job planning hinders their full potential. To address this, the paper proposes a human–drone collaborative inspection approach leveraging flexible surface electromyography (sEMG) for conveying inspectors' speech guidance to intelligent drones. Specifically, this paper contributes a new data set, sEMG Commands for Piloting Drones (sCPD), and an sEMG‐based Cross‐subject Classification Network (sXCNet), for both command keyword recognition and inspector identification. sXCNet acquires the desired functions and performance through a synergetic effort of sEMG signal processing, spatial‐temporal‐frequency deep feature extraction, and multitasking‐enabled cross‐subject representation learning. The cross‐subject design permits deploying one unified model across all authorized inspectors, eliminating the need for subject‐dependent models tailored to individual users. sXCNet achieves notable classification accuracies of 98.1% on the sCPD data set and 86.1% on the public Ninapro db1 data set, demonstrating strong potential for advancing sEMG‐enabled human–drone collaboration in road infrastructure inspection.

中文翻译：

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一种基于表面肌电图的深度学习模型，用于指导半自动无人机进行道路基础设施检查

虽然半自动无人机越来越多地用于道路基础设施检查，但它们在初始工作规划之外独立处理复杂场景的能力不足，阻碍了它们的全部潜力。为了解决这个问题，本文提出了一种人机协作检查方法，利用柔性表面肌电图 （sEMG） 将检查员的语音指导传达给智能无人机。具体来说，本文提供了一个新的数据集，用于驾驶无人机的 sEMG 命令 （sCPD） 和基于 sEMG 的跨主体分类网络 （sXCNet），用于命令关键字识别和检查器识别。sXCNet 通过 sEMG 信号处理、时空频率深度特征提取和多任务支持的跨主体表示学习的协同努力来获得所需的功能和性能。跨受试者设计允许在所有授权检查员中部署一个统一的模型，无需为个人用户量身定制的受试者相关模型。sXCNet 在 sCPD 数据集上实现了 98.1% 的显着分类精度，在公共 Ninapro db1 数据集上实现了 86.1% 的分类精度，展示了在道路基础设施检查中推进 sEMG 支持的人机协作的巨大潜力。