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Deep learning-based object identification with instance segmentation and pseudo-LiDAR point cloud for work zone safety
Computer-Aided Civil and Infrastructure Engineering ( IF 9.6 ) Pub Date : 2021-08-04 , DOI: 10.1111/mice.12749
Jie Shen 1 , Wenjie Yan 1 , Peng Li 1 , Xin Xiong 1
Affiliation  

Automated object identification in three-dimensional (3D) space is crucial for work zone safety, such as compliance with construction rules and preventing workplace injuries and deaths. However, it is greatly challenged by some factors like high-quality detection, high-quality instance segmentation, few engineering object datasets with masks, and accurate 3D object understanding due to scale variations and limited cues in the 3D world. Traditional hand-crafted methods suffer from these challenges. Our key insight is to use 2D object detection, instance segmentation and camera vision to compute pseudo-light detection and ranging (LiDAR) point cloud for 3D object identification. On the one hand, an enhanced feature pyramid network is proposed to extract more fine-grained object features, and an improved cascade mask R-CNN is applied to detect bounding boxes and masks for all 2D objects efficiently. Moreover, the AIM dataset for heavy equipment detection is augmented, and a new object class with the bounding box and mask is added. On the other hand, pseudo-LiDAR point clouds of objects based on bounding boxes and masks are recovered from a monocular image by deep learning, automatic camera parameter estimation, vision-based method, and space filter. Extensive experiments and analyses show that the new methodology can identify 3D objects and automatically analyze work zone safety. The proposed object detection model has achieved state-of-the-art results on the AIM dataset and 97.2% in mean average precision for the augmented dataset. The collision detection model using pseudo-LiDAR point cloud has obtained 95.99% in accuracy. The new model will serve as a baseline to support 3D object identification research for other 3D tasks.

中文翻译:

基于深度学习的物体识别,具有实例分割和伪激光雷达点云,用于工作区安全

三维 (3D) 空间中的自动物体识别对于工作区安全至关重要,例如遵守施工规则和防止工作场所受伤和死亡。然而,由于 3D 世界中的尺度变化和有限线索,它受到一些因素的极大挑战,例如高质量检测、高质量实例分割、带有掩码的工程对象数据集很少以及准确的 3D 对象理解。传统的手工制作方法面临这些挑战。我们的主要见解是使用 2D 对象检测、实例分割和相机视觉来计算用于 3D 对象识别的伪光检测和测距 (LiDAR) 点云。一方面,提出了一种增强的特征金字塔网络来提取更细粒度的对象特征,并应用改进的级联掩码 R-CNN 来有效检测所有 2D 对象的边界框和掩码。此外,增加了用于重型设备检测的 AIM 数据集,并添加了具有边界框和掩码的新对象类。另一方面,通过深度学习、自动相机参数估计、基于视觉的方法和空间过滤器从单目图像中恢复基于边界框和掩码的对象的伪激光雷达点云。大量实验和分析表明,新方法可以识别 3D 对象并自动分析工作区安全。所提出的目标检测模型在 AIM 数据集上取得了最先进的结果,在增强数据集上的平均精度达到了 97.2%。使用伪激光雷达点云的碰撞检测模型的准确率达到了 95.99%。
更新日期:2021-08-04
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