YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO
By Muhammad Hussain. IEEE Access, 2024
421 citations recorded by the source corpus.
This paper implements a systematic methodological approach to review the evolution of YOLO variants. Each variant is dissected by examining its internal architectural composition, providing a thorough understanding of its structural components. Subsequently, the review highlights key architectural innovations introduced in each variant, shedding light on the incremental refinements. The review includes benchmarked performance metrics, offering a quantitative measure of each variant’s capabilities. The paper further presents the performance of YOLO variants across a diverse range of domains, manifesting their real-world impact. This structured approach ensures a comprehensive examination of YOLOs journey, methodically communicating its internal advancements and benchmarked performance before delving into domain applications. It is envisioned, the incorporation of concepts such as federated learning can introduce a collaborative training paradigm, where YOLO models benefit from training across multiple edge devices, enhancing privacy, adaptability, and generalisation.
Subfields: Epidemiology, Genetics
View Paper: https://www.semanticscholar.org/paper/6ccd0eb94f37c00ae49de81b2752dbb9bd6b0c3b
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Citations in the corpus
- The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024, neutral): “To responsibly harness YOLO’s potential, we must delve into these ethical challenges with a nuanced understanding.”
- What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector (2024, background): “Central to this domain is object detection, a critical task that involves accurately identifying and localizing objects within images or video sequences [2].”