Fatigue of operators due to intensive workloads and long working time is a significant constraint that leads to inefficient crane operations and increased risk of safety issues. It can be potentially prevented through early warnings of fatigue for further appropriate work shift arrangements. Many deep neural networks have recently been developed for the fatigue detection of vehicle drivers through training and processing the facial image or video data from the public driver's datasets. However, these datasets are difficult to directly use for the fatigue detections under crane operation scenarios due to the variations of facial features and head movement patterns between crane operators and vehicle drivers. Furthermore, there is no representative and public dataset with the facial information of crane operators under construction scenarios. Therefore, this study aims to explore and analyse the features of multi-sources datasets and the corresponding data acquisition methods which are suitable for crane operators' fatigue detection, further providing collection guidelines of crane operators dataset. Variations on public datasets such as real or pretend facial expression, the segment level of human-verified labelling, camera positions, acquisition scenarios, and illumination conditions are analysed. A hybrid learning architecture is proposed by combining convolutional neural networks (CNN) and long short-term memory (LSTM) for fatigue detection. In order to establish a unified evaluation criterion, the effort of the study includes relabelling three public vehicle drivers datasets, NTHU-DDD, UTA-RLDD, and YawnDD, with human-verified labels at the frame and minute segment levels, and training the corresponding hybrid fatigue detection models accordingly. The average detection accuracies and losses are identified for the trained models of UTA-RLDD, NTHU-DDD, and YawnDD individually. The trained models are used to evaluate the fatigue status of facial videos from licensed crane operators under simulated crane operation scenarios. The results suggest the necessary considerations of different influential factors for establishing a large and public fatigue dataset for crane operators.

由于工作量大和工作时间长，操作员的疲劳是一个重要的限制因素，会导致起重机操作效率低下并增加安全问题的风险。可以通过疲劳的早期预警以进一步适当的轮班安排来预防这种情况。最近开发了许多深度神经网络，通过训练和处理来自公共驾驶员数据集的面部图像或视频数据来检测车辆驾驶员的疲劳。然而，由于起重机操作员和车辆驾驶员之间的面部特征和头部运动模式的变化，这些数据集难以直接用于起重机操作场景下的疲劳检测。此外，在施工场景下，没有具有代表性的公共数据集，其中包含起重机操作员的面部信息。因此，本研究旨在探索和分析多源数据集的特征以及适用于起重机操作员疲劳检测的相应数据采集方法，进一步为起重机操作员数据集的采集提供指导。分析了公共数据集的变化，例如真实或假装的面部表情、人类验证标记的分段级别、相机位置、采集场景和照明条件。通过将卷积神经网络 (CNN) 和长短期记忆 (LSTM) 相结合，提出了一种用于疲劳检测的混合学习架构。为了建立统一的评估标准，该研究的工作包括重新标记三个公共车辆驾驶员数据集 NTHU-DDD、UTA-RLDD 和 YawnDD，并在帧和分钟段级别使用人工验证的标签，并相应地训练相应的混合疲劳检测模型。分别为 UTA-RLDD、NTHU-DDD 和 YawnDD 的训练模型确定平均检测精度和损失。经过训练的模型用于在模拟起重机操作场景下评估来自有执照的起重机操作员的面部视频的疲劳状态。结果表明，在为起重机操作员建立大型公共疲劳数据集时，必须考虑不同的影响因素。经过训练的模型用于在模拟起重机操作场景下评估来自有执照的起重机操作员的面部视频的疲劳状态。结果表明，在为起重机操作员建立大型公共疲劳数据集时，必须考虑不同的影响因素。经过训练的模型用于在模拟起重机操作场景下评估来自有执照的起重机操作员的面部视频的疲劳状态。结果表明，在为起重机操作员建立大型公共疲劳数据集时，必须考虑不同的影响因素。