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arXiv · 2610.41673

A Self-Supervised Pose Estimation Approach for Construction Machines

Alaa Alshubbak·Daniel Görges
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Abstract

Pose estimation is a computer vision task used to estimate a skeleton of dynamic systems to predict future movements. Most of the research in this direction is based on a supervised learning approach which requires a massive amount of labeled datasets. In this paper, a self-supervised three-stage model based on a contrastive learning approach is introduced for estimating a skeleton of dynamic construction machines; such as excavators without using any labeled images for the first stage. The whole model structure is divided into three stages: the pre-train stage using the SimCLR contrastive approach, and two fine-tuning stages for the transfer learning and the downstream task. The model can leverage the features and learn from a huge unlabeled dataset called ACID to two small datasets generated from NVIDIA Isaac and MATLAB Simscape simulators as well as transfer the knowledge to a smaller dataset with a ratio of 3.5% from the original ACID dataset. The results show that the proposed approach can improve the accuracy of pose estimation for heavy construction machines in real images by 11% and 13% in comparison to the normal self-supervised approach with two backbones ResNet-50 and HRNet-W32, respectively.

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Alaa Alshubbak, Daniel Görges. 2026-10-30. A Self-Supervised Pose Estimation Approach for Construction Machines. https://doi.org/10.1007/978-3-031-47966-3_31

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