| Summary: | Research on object detection methods (ODM) has increased over the past decades due to their practical implementations across various sectors. The growing demand for better ODM in real situations has catalysed its advancements in academic research and publications, making it challenging to track progress. Bibliometric analysis offers an effective method to summarise these advancements efficiently. It is valuable for visualising and identifying a comprehensive ODM research structure and overview. However, despite the high volume of ODM publications since 2014, bibliometric analyses in this field remain limited. Hence, this study analysed the ODM research landscape using bibliometric analysis, highlighting imperative materials for initial reference and emphasising the apparent ODM topics commonly discussed. The bibliometric data for this study was retrieved from the Web of Science database using a configured search query. VOSviewer software analysed the data collected with performance analysis and science mapping. The findings reveal that “Foundational Architectural and Data Processing Tasks of Object Detection Methods” is the most prominent ODM theme that employs statistical models within the detection framework. Additionally, this study suggests the integration of probabilistic inference approaches with ODM to quantify the prediction uncertainties. One of the probabilistic inference approaches, nonparametric predictive inference, potentially improves detection accuracy, which is another popular theme in ODM studies. This study also identifies autonomous detection applications as one of the emerging trends within the thematic clusters. These insights guide researchers who seek to navigate the evolving ODM research areas, particularly in contributing to ODM progress for more adaptable and efficient detections.
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