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PurposeThe safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.Design/methodology/approachThis model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.FindingsThe experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.Originality/valueThis study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

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With the rapid development, Microblog as an important interactive media, has become a kind of transmission carrier of the false information. Therefore, the research significance of Micro-blog information credibility becomes more and more important today. In this paper, different representative factors are selected from three facets--text contents, information dissemination and information source--which influence the information credibility of Micro-blog. We choose Netica software to build Bayesian network model and use the rumors grabbed from Sina Weibo as experimental data in order to get the relationship between conditions and phenomena from the changes of probability distribution in Bayesian network. On the basis of this, we find the influences of the representative factors on the subjective credibility of objective unreliable information.