Title: YOLO evolution: a comprehensive review and bibliometric analysis of object detection advancements

Authors: Annu Dabas; Ekta Narwal

Addresses: Department of Mathematics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India ' Department of Mathematics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India

Abstract: In recent years, much progress has been made in real-time object detection algorithms. This paper reviewed various versions of one such algorithm, you only look once (YOLO), ranging from YOLOv1 to YOLOv8. We have briefly mentioned the significant improvements made in these versions and compared their performance using the average precision (AP) metric. We then carried out a bibliometric analysis of the research publications mentioning "you only look once" or "yolo" in their title, abstract or keywords in Scopus and Web of Science databases and title or abstract in Dimensions database from 2016 to mid-2024, which involved number of publications in each year, publication count from each country, distribution of publication in various disciplines, co-authorship analysis, and cooccurrence analysis of the keywords mentioned in these publications. We concluded the areas where this algorithm is widely used and where more research is still required.

Keywords: YOLO; you only look once; object detection; computer vision; ML; machine learning; deep learning; CNN; convolutional neural network; bibliometric research.

DOI: 10.1504/IJSISE.2024.143821

International Journal of Signal and Imaging Systems Engineering, 2024 Vol.13 No.3, pp.133 - 156

Received: 12 Feb 2024
Accepted: 06 Sep 2024
Published online: 08 Jan 2025 *

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