The invention discloses a driver distraction detection method based on a fusion attention mechanism and Bi-LSTM, and the method comprises the steps: collecting an original image of a driver, carrying out the data preprocessing of the original image of the driver, and obtaining a target data set; constructing a driver distraction detection network model based on a fusion attention mechanism and Bi-LSTM, and inputting the target data set into the driver distraction detection network model for model training to obtain a trained model; evaluating the overall performance of the trained model according to the performance index, and obtaining a target detection model with the driver distraction detection capability; and the target detection model is deployed and migrated to an actual vehicle-mounted environment of the new energy vehicle, driver behaviors are detected in real time, and a real-time detection feedback result is obtained. According to the invention, the accuracy of distraction behavior detection is improved.

    本发明公开了一种基于融合注意力机制和Bi‑LSTM的驾驶员分心检测方法,包括:采集驾驶员原始图像并对驾驶员原始图像进行数据预处理,获得目标数据集;构建基于融合注意力机制和Bi‑LSTM的驾驶员分心检测网络模型,将目标数据集输入驾驶员分心检测网络模型进行模型训练,获得训练后的模型;根据性能指标对训练后的模型的整体性能进行评估,获得具备驾驶员分心检测能力的目标检测模型;将目标检测模型部署迁移到实际的新能源车辆的车载环境中,实时检测驾驶员行为,获得实时检测反馈结果。本发明提高了对分心行为检测的准确性。


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    Title :

    Driver distraction detection method based on fusion attention mechanism and Bi-LSTM


    Additional title:

    一种基于融合注意力机制和Bi-LSTM的驾驶员分心检测方法


    Contributors:
    FENG SANG (author) / ZENG HUILIN (author) / CHEN YANYANG (author) / HUANG XIAOTAO (author)

    Publication date :

    2025-03-07


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06V / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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