A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes

Abstract

Due to uncontrollable influences of the manufacturing process and different construction environments, there are significant challenges to extracting accurate positioning points for the lifting holes in prefabricated beams. In this study, we propose a two-stage feature detection, which comprises the ADD (multi-Attention DASPP DeeplabV3+) model and the VLFGM (Voting mechanism line fitting based on Gaussian mixture model) method. Initially, the YoloV5s model is employed for image coarse localization to reduce the impacts of background noise, and the ADD model follows to segment the target region. Then, the multi-step ECA mechanism is introduced to the ADD. It can mitigate the loss of interest features in the pooling layer of the backbone as well as retain the details of the original features; DASPP is adopted to fuse features at different scales to enhance the correlation of features among channels. Finally, VLFGM is utilized to reduce the dependency of accuracy on segmentation results. The experimental results demonstrate that the proposed model achieves a mean intersection over union (mIoU) of 95.07%, with a 3.48% improvement and a mean pixel accuracy (mPA) of 99.16% on the validation set. The improved method reduces vertexes error by 30.00% (to 5.39 pixels) and centroid error by 28.93% (to 1.72 pixels), which exhibits superior stability and accuracy. This paper provides a reliable solution for visual positioning of prefabricated beams in complex environments.

Keywords

• alignment feature points
• DeepLabV3+
• line fitting
• visual lifting
• YoloV5s