A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond

By Juan R. Terven, Diana-Margarita Córdova-Esparza. arXiv.org, 2023

524 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a11cdd96e030ba7a4fe7f885de8c0a739af4bae6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. J. Hong
2. Dillon Reis
3. Ahmad Daoudi
4. Jordan Kupec

In Neurology

1. J. Hong
2. Dillon Reis
3. Ahmad Daoudi
4. Jordan Kupec

In Radiology, Nuclear Medicine and Imaging

1. J. Hong
2. Dillon Reis
3. Ahmad Daoudi
4. Jordan Kupec

Citations in the corpus

• Real-Time Flying Object Detection with YOLOv8 (2023, methodology): “…connections enable the architecture to achieve a richer gradient flow while reducing computation as described [20] proposed by Wang et al. The neck [18], as described by Teven et al., of YOLOv5 connects the backbone to the head, whose purpose is to aggregate and refine the features extracted by the…”