Autonomous equipment is playing an increasingly important role in construction tasks. It is essential to equip autonomous equipment with powerful 3D detection capability to avoid accidents and inefficiency. However, there is limited research within the construction field that has extended detection to 3D. To this end, this study develops a light detection and ranging (LiDAR)-based deep-learning model for the 3D detection of workers on construction sites. The proposed model adopts a voxel-based anchor-free 3D object detection paradigm. To enhance the feature extraction capability for tough detection tasks, a novel Transformer-based block is proposed, where the multi-head self-attention is applied in local grid regions. The detection model integrates the Transformer blocks with 3D sparse convolution to extract wide and local features while pruning redundant features in modified downsampling layers. To train and test the proposed model, a LiDAR point cloud dataset was created, which includes workers in construction sites with 3D box annotations. The experiment results indicate that the proposed model outperforms the baseline models with higher mean average precision and smaller regression errors. The method in the study is promising to provide worker detection with rich and accurate 3D information required by construction automation.

中文翻译：

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具有局部稀疏变压器的深度学习框架，用于使用 LiDAR 进行 3D 建筑工人检测


自主设备在施工任务中发挥着越来越重要的作用。为自主设备配备强大的3D检测能力至关重要，以避免事故和低效率。然而，建筑领域内将检测扩展到 3D 的研究有限。为此，本研究开发了一种基于光检测和测距 (LiDAR) 的深度学习模型，用于对建筑工地工人进行 3D 检测。所提出的模型采用基于体素的无锚 3D 对象检测范例。为了增强困难检测任务的特征提取能力，提出了一种基于 Transformer 的新型块，其中多头自注意力应用于局部网格区域。该检测模型将 Transformer 块与 3D 稀疏卷积集成在一起，以提取宽域和局部特征，同时修剪修改后的下采样层中的冗余特征。为了训练和测试所提出的模型，创建了一个 LiDAR 点云数据集，其中包括带有 3D 框注释的建筑工地工人。实验结果表明，所提出的模型优于基线模型，具有更高的平均精度和更小的回归误差。该研究中的方法有望为工人检测提供建筑自动化所需的丰富且准确的 3D 信息。