Trajectory Prediction of Assembly Alignment of Columnar Precast Concrete Members with Deep Learning

• Main Authors: Ke Zhang, Shenghao Tong, Huaitao Shi
• Format: Article
• Language: English
• Published: MDPI AG 2019-05-01
• Series: Symmetry
• Subjects: trajectory prediction; image processing; object detection; deep learning; assembly alignment
• Online Access: https://www.mdpi.com/2073-8994/11/5/629

During the construction of prefabricated building, there are some problems such as a time consuming, low-level of automation when precast concrete members are assembled and positioned. This paper presents vision-based intelligent assembly alignment guiding technology for columnar precast concrete members. We study the video images of assembly alignment of the hole at the bottom of the precast concrete members and the rebar on the ground. Our goal is to predict the trajectory of the moving target in a future moment and the movement direction at each position during the alignment process by assembly image sequences. However, trajectory prediction is still subject to the following challenges: (1) the effect of external environment (illumination) on image quality; (2) small target detection in complex backgrounds; (3) low accuracy of trajectory prediction results based on the visual context model. In this paper, we use mask and adaptive histogram equalization to improve the quality of the image and improved method to detect the targets. In addition, aiming at the low position precision of trajectory prediction based on the context model, we propose the end point position-matching equation according to the principle of end point pixel matching of the moving target and fixed target, as the constraint term of the loss function to improve the prediction accuracy of the network. In order to evaluate comprehensively the performance of the proposed method on the trajectory prediction in the assembly alignment task, we construct the image dataset, use Hausdorff distance as the evaluation index, and compare with existing prediction methods. The experimental results show that, this framework is better than the existing methods in accuracy and robustness at the prediction of assembly alignment motion trajectory of columnar precast concrete members.