FieldNet: Efficient Real-Time Shadow Removal for Enhanced Vision in Field Robotics
This addresses the challenge of shadows hindering object detection and localization in field robotics, with incremental improvements in efficiency and accuracy for practical applications like precision agriculture.
The paper tackles the problem of shadow removal in outdoor computer vision for field robotics by introducing FieldNet, a deep learning framework that achieves up to 9x speed improvements (66 FPS) and superior quality (PSNR: 38.67, SSIM: 0.991) compared to state-of-the-art methods.
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 $9$x 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.