FieldNet: Efficient real-time shadow removal for enhanced vision in field robotics
Year: 2025
Type: article
Abstract: Shadows significantly hinder computer vision tasks in outdoor environments, particularly in field robotics, where varying lighting conditions complicate object detection and localization. We present FieldNet, a novel deep learning framework for real-time shadow removal, optimized 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. • FieldNet: Novel deep learning for shadow removal in robotics. • Real-time: 66 fps on Nvidia 2080Ti for outdoor applications. • New loss boosts shadow boundary accuracy with context. • Dataset: 10,000 images with synthetic shadows for robustness. • Outperforms state-of-the-art on three benchmark datasets.
Source: Expert Systems with Applications
Institutions: James Cook University, QLT (Canada)
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,500
OpenAlex ID: W4409165247