# FieldNet: Efficient Real-Time Shadow Removal for Enhanced Vision in Field Robotics

arXiv: https://arxiv.org/abs/2403.08142 · HTML: https://mlsanity.com/paper/2403.08142/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https://mlsanity.com/paper/1712.02478/) (2017)
- [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019)
- [Shadow Removal via Shadow Image Decomposition](https://mlsanity.com/paper/1908.08628/) (2019)
- [Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data](https://mlsanity.com/paper/1903.10683/) (2019)
- [Auto-Exposure Fusion for Single-Image Shadow Removal](https://mlsanity.com/paper/2103.01255/) (2021)
- [DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network](https://mlsanity.com/paper/2207.10434/) (2022)
- [Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification](https://mlsanity.com/paper/2010.05785/) (2020)
- [From Shadow Segmentation to Shadow Removal](https://mlsanity.com/paper/2008.00267/) (2020)
- [Direction-aware Spatial Context Features for Shadow Detection and Removal](https://mlsanity.com/paper/1805.04635/) (2018)
- [From Shadow Generation to Shadow Removal](https://mlsanity.com/paper/2103.12997/) (2021)
- [ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal](https://mlsanity.com/paper/1908.01323/) (2019)
- [Physics-based Shadow Image Decomposition for Shadow Removal](https://mlsanity.com/paper/2012.13018/) (2020)
- [RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal](https://mlsanity.com/paper/1911.09178/) (2019)
- [Latent Feature-Guided Diffusion Models for Shadow Removal](https://mlsanity.com/paper/2312.02156/) (2023)
- [Learning from Synthetic Shadows for Shadow Detection and Removal](https://mlsanity.com/paper/2101.01713/) (2021)
- [Learning Physical-Spatio-Temporal Features for Video Shadow Removal](https://mlsanity.com/paper/2303.09370/) (2023)
- [Progressive Recurrent Network for Shadow Removal](https://mlsanity.com/paper/2311.00455/) (2023)

## Built on by (most cited first, 4 of 4)

- [Too Easily Fooled? Prompt Injection Breaks LLMs on Frustratingly Simple Multiple-Choice Questions](https://mlsanity.com/paper/2508.13214/) (2025)
- [Advancements in Weed Mapping: A Systematic Review](https://mlsanity.com/paper/2507.01269/) (2025)
- [Weed Detection in Challenging Field Conditions: A Semi-Supervised Framework for Overcoming Shadow Bias and Data Scarcity](https://mlsanity.com/paper/2508.19511/) (2025)
- [ODDR: One-Step Deshadow Diffusion via Reward Guidance](https://mlsanity.com/paper/2610.01291/) (2026)

## Abstract (arXiv)

Shadows significantly hinder computer vision tasks in outdoor environments, particularly in field robotics, where varying lighting conditions complicate object detection and localisation. We present FieldNet, a novel deep learning framework for real-time shadow removal, optimised for resource-constrained hardware. FieldNet introduces a probabilistic enhancement module and a novel loss function to address challenges of inconsistent shadow boundary supervision and artefact generation, achieving enhanced accuracy and simplicity without requiring shadow masks during inference. Trained on a dataset of 10,000 natural images augmented with synthetic shadows, FieldNet outperforms state-of-the-art methods on benchmark datasets (ISTD, ISTD+, SRD), with up to 9x speed improvements (66 FPS on Nvidia 2080Ti) and superior shadow removal quality (PSNR: 38.67, SSIM: 0.991). Real-world case studies in precision agriculture robotics demonstrate the practical impact of FieldNet in enhancing weed detection accuracy. These advancements establish FieldNet as a robust, efficient solution for real-time vision tasks in field robotics and beyond.