Excavator 2D Pose Estimation
Description
This project utilizes High Representation Network, a state-of-the-art convolutional neural network architecture to estimate the 2D keypoints defining the full body pose of excavators from images.
This repo is part of the publication "A Assadzadeh et al., 'Vision-based excavator pose estimation using synthetically generated datasets with domain randomization', Automation in Construction, 2022" that utilizes synthetically generated datasets to train a deep learning model for excavator pose estimation.
Demo
Demo of the model trained on a combination of synthetic and real images
Install
$ git clone https://github.com/N1M49/ExcPose2D.git
$ cd ExcPose2D
$ pip install -r requirements.txt
Quick Start Examples
Training
$ python train.py --dataset_dir './datasets/FDR_1k' -p './pretrained_models/pose_hrnet_w48_384x288.pth' --vis_enabled False
Evaluation
$ python val.py --dataset './datasets/eval/RealSet_test_debug' --weights './experiments/archived/Vis0_FDR_15k_best.pth' --pck_thr 0.05