CWPR:基于变压器的优化模型,用于建筑机器人上建筑工人的姿势估计

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jiakai Zhou , Wanlin Zhou , Yang Wang
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引用次数: 0

摘要

估计建筑工人的姿势对于识别不安全行为、进行人体工程学分析和评估生产率至关重要。最近,利用建筑机器人捕捉 RGB 图像进行姿势估算提供了灵活的监控视角和及时的干预措施。然而,现有的多人姿态估计(MHPE)方法很难在准确性和速度之间取得平衡,因此不适合在建筑机器人上进行实时应用。本文介绍了建筑工人姿态识别器(CWPR),这是一种为建筑机器人量身定制的基于变压器的优化 MHPE 模型。具体来说,CWPR 利用配备多尺度特征融合模块的轻量级编码器来提高运行速度。然后,采用交叉联合(IoU)感知查询选择策略,为混合解码器提供高质量的初始查询,从而显著提高性能。此外,解码器去噪模块用于将有噪声的地面实况纳入解码器,从而减轻样本失衡,进一步提高准确性。此外,建筑工人姿势和动作(CWPA)数据集是从真实建筑场景中捕获的 154 个视频中收集的。该数据集针对不同的任务进行了注释:用于 MHPE 的姿势基准和用于动作识别的动作基准。实验证明,CWPR 实现了最高级别的准确率和最快的推理速度,在 COCO 测试集上实现了 68.1 的平均精度(AP),处理时间为 26 毫秒;在 CWPA 姿势基准上实现了 76.2 的平均精度(AP),处理时间为 21 毫秒。此外,当与建筑机器人硬件上的动作识别方法 ST-GCN 集成时,CWPR 在 CWPA 动作基准上实现了 78.7 的平均精度和 19 毫秒的处理时间,验证了其在实际部署中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CWPR: An optimized transformer-based model for construction worker pose estimation on construction robots
Estimating construction workers’ poses is critically important for recognizing unsafe behaviors, conducting ergonomic analyses, and assessing productivity. Recently, utilizing construction robots to capture RGB images for pose estimation offers flexible monitoring perspectives and timely interventions. However, existing multi-human pose estimation (MHPE) methods struggle to balance accuracy and speed, making them unsuitable for real-time applications on construction robots. This paper introduces the Construction Worker Pose Recognizer (CWPR), an optimized Transformer-based MHPE model tailored for construction robots. Specifically, CWPR utilizes a lightweight encoder equipped with a multi-scale feature fusion module to enhance operational speed. Then, an Intersection over Union (IoU)-aware query selection strategy is employed to provide high-quality initial queries for the hybrid decoder, significantly improving performance. Besides, a decoder denoising module is used to incorporate noisy ground truth into the decoder, mitigating sample imbalance and further improving accuracy. Additionally, the Construction Worker Pose and Action (CWPA) dataset is collected from 154 videos captured in real construction scenarios. The dataset is annotated for different tasks: a pose benchmark for MHPE and an action benchmark for action recognition. Experiments demonstrate that CWPR achieves top-level accuracy and the fastest inference speed, attaining 68.1 Average Precision (AP) with a processing time of 26 ms on the COCO test set and 76.2 AP with 21 ms on the CWPA pose benchmark. Moreover, when integrated with the action recognition method ST-GCN on construction robot hardware, CWPR achieves 78.7 AP and a processing time of 19 ms on the CWPA action benchmark, validating its effectiveness for practical deployment.
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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