A decade of research in small object detection: a comprehensive bibliometric analysis

Abstract Small or tiny object detection has gained significant attention from researchers in recent years due to its wide range of applications across various domains. This highlights its status as a promising research area. The study employs bibliometric analysis to understand the current research status and future trends in small or tiny object detection. By analyzing documents published between 2015 and 2024 from the Scopus database, we map the quantitative structure of this research area, identify the most influential publications and key contributions, and uncover potential research directions. The contribution of this paper is to provide a comprehensive bibliometric analysis of a decade of research on small or tiny object detection, a topic that has not been previously published. A total of 1627 publications were analyzed. Our findings reveal a significant increase in the number of publications over the decade, indicating growing research interest. China leads the field, contributing the majority of publications, followed by the USA and South Korea. Foundational contributions, such as "Inside-Outside Net" and GAN-based approaches, are highlighted for their impact on detection methodologies. Key topics include deep learning, convolutional neural networks, feature extraction, and object recognition. The analysis also identifies major challenges such as data scarcity, computational efficiency, and environmental complexities like occlusion and background clutter. Emerging trends include lightweight architectures, multiscale feature fusion, and edge computing, reflecting a shift toward resource-efficient implementations for practical applications. This study provides foundational insights into the current state of the field and highlights directions for future research, aiming to guide scholars and practitioners in advancing the technology of small or tiny object detection.