Title: A review of advancements in deep learning-based shadow detection and removal in image and video analysis

Authors: Hui Liu; Kin Sam Yen

Addresses: School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia ' School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia

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.

Keywords: shadow detection; shadow removal; deep learning; shadow image analysis; video analysis.

DOI: 10.1504/IJIEI.2024.138851

International Journal of Intelligent Engineering Informatics, 2024 Vol.12 No.2, pp.135 - 168

Received: 26 Sep 2023
Accepted: 29 Jan 2024
Published online: 31 May 2024 *

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