Depth Estimation Using Weighted-Loss and Transfer Learning

Muhammad Hafeez, Michael Madden, Michael Madden, Ganesh Sistu, Ihsan Ullah, Ihsan Ullah

2024

Abstract

Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation method mainly relies on the chosen loss function, the model architecture, quality of data and performance metrics. In this study, we propose a simplified and adaptable approach to improve depth estimation accuracy using transfer learning and an optimized loss function. The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge Loss and Structural Similarity Index (SSIM). We use a grid search and a random search method to find optimized weights for the losses, which leads to an improved model. We explore multiple encoder-decoder-based models including DenseNet121, DenseNet169, DenseNet201, and EfficientNet for the supervised depth estimation model on NYU Depth Dataset v2. We observe that the EfficientNet model, pre-trained on ImageNet for classification when used as an encoder, with a simple upsampling decoder, gives the best results in terms of RSME, REL and log 10 : 0.386, 0.113 and 0.049, respectively. We also perform a qualitative analysis which illustrates that our model produces depth maps that closely resemble ground truth, even in cases where the ground truth is flawed. The results indicate significant improvements in accuracy and robustness, with EfficientNet being the most successful architecture.

Paper Citation

in Harvard Style

Hafeez M., Madden M., Sistu G. and Ullah I. (2024). Depth Estimation Using Weighted-Loss and Transfer Learning. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 780-787. DOI: 10.5220/0012461300003660

in Bibtex Style

@conference{visapp24,
author={Muhammad Hafeez and Michael Madden and Ganesh Sistu and Ihsan Ullah},
title={Depth Estimation Using Weighted-Loss and Transfer Learning},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={780-787},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012461300003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Depth Estimation Using Weighted-Loss and Transfer Learning
SN - 978-989-758-679-8
AU - Hafeez M.
AU - Madden M.
AU - Sistu G.
AU - Ullah I.
PY - 2024
SP - 780
EP - 787
DO - 10.5220/0012461300003660
PB - SciTePress