A Self-Supervised Pose Estimation Approach for Construction Machines

The paper introduces a self‑supervised, three‑stage pose estimation model for construction machines, leveraging contrastive learning (SimCLR) in the pre‑train stage and two fine‑tuning stages for transfer learning and downstream tasks. It trains on a large unlabeled ACID dataset and fine‑tunes on smaller simulated datasets, achieving 11% and 13% accuracy improvements over baseline self‑supervised models using ResNet‑50 and HRNet‑W32 backbones, respectively.

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• computer-vision
• fine-tuning