A review of advancements in deep learning-based shadow detection and removal in image and video analysis
by Hui Liu; Kin Sam Yen
International Journal of Intelligent Engineering Informatics (IJIEI), Vol. 12, No. 2, 2024

Abstract: Shadow detection and removal are vital in image processing and computer vision applications, and have impacted diverse industries such as automated driving, medicine, and agriculture. Shadows in images and videos can significantly impede algorithm performance. Numerous models and techniques have been proposed to address this concern. This study presents a comprehensive review and analysis of research focusing on shadow detection and removal, including image - and video-based algorithms related to deep learning (DL) approaches since 2017. The aim is to explore the latest advancements and developments in integrating deep learning approaches in this field. The architecture of the DL models and their performance are analysed. A noticeable trend is observed as shadow detection and removal algorithms transition from conventional image processing and analysis methods to DL approaches. The challenges, such as data scarcity, training paradigms, incorporation of temporal information, and utilisation of advanced models, remain open research areas.

Online publication date: Fri, 31-May-2024

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