FieldNet: Efficient real-time shadow removal for enhanced vision in field robotics

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.