Authors:
Estephan Rustom
;
Henrique Cabral
;
Sreeraj Rajendran
and
Elena Tsiporkova
Affiliation:
EluciDATA Lab, Sirris, Bd A. Reyerslaan 80 1030, Brussels, Belgium
Keyword(s):
Shadow Detection, Unsupervised Learning, Deep Learning, CNN.
Abstract:
Accurate shadow detection and correction are critical for improving image classification and segmentation but remain challenging due to the lack of well-labeled datasets and the context-specific nature of shadows, which limit the generalizability of supervised models. Existing unsupervised approaches, on the other hand, often require specialized data or are computationally intensive due to high parameterization. In this paper, we introduce ShadowScout, a novel, low-parameterized, unsupervised deep learning method for shadow detection using standard RGB images. ShadowScout is fast, achieves performance comparable to state-of-the-art supervised methods, and surpasses existing unsupervised techniques across various datasets. Additionally, the model can seamlessly incorporate extra data, such as near-infrared channels, to enhance shadow detection accuracy further. ShadowScout is available on the authors’ GitHub repository (https://github.com/EluciDATALab/elucidatalab.starterkits/tree/ ma
in/models/shadows).
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