A Review of YOLO Object Detection Based on Deep Learning

SHAO Yanhua; ZHANG Duo; CHU Hongyu; ZHANG Xiaoqiang; RAO Yunbo

doi:10.11999/JEIT210790

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3697-3708

SHAO Yanhua, ZHANG Duo, CHU Hongyu, ZHANG Xiaoqiang, RAO Yunbo. A Review of YOLO Object Detection Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3697-3708. doi: 10.11999/JEIT210790

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SHAO Yanhua, ZHANG Duo, CHU Hongyu, ZHANG Xiaoqiang, RAO Yunbo. A Review of YOLO Object Detection Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3697-3708. doi: 10.11999/JEIT210790

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A Review of YOLO Object Detection Based on Deep Learning

doi: 10.11999/JEIT210790

1.
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
2.
University of Electronic Science & Technology of China, Chengdu 610054, China

Funds: The National Natural Science Foundation of China (61601382), Sichuan Provincial Science and Technology Project (2019YJ0325, 2020YFG0148, 2021YFG0314)

Received Date: 2021-08-06
Accepted Date: 2022-02-16
Rev Recd Date: 2022-01-22

Available Online: 2022-02-19

Publish Date: 2022-10-19

Abstract

Abstract

Object detection is one of the basic tasks and research hotspots in the field of computer vision. The YOLO (You Only Look Once) frames object detection is a regression problem to implement end-to-end training and detection. YOLO becomes the leading object detector due to its good speed-accuracy balance, which has been successfully studied, improved, and applied to many different fields. YOLO series and its important improvements and applications are investigated in detail. Firstly, the YOLO family and important improvements are systematically summarized, including YOLOv1-v4, YOLOv5, Scaled-YOLOv4, YOLOR, and the latest YOLOX. Then, important backbone and loss functions in YOLO are analyzed and summarized in detail. Next, the application of YOLO is systematically classified and summarized according to different improvement ideas or scenarios, such as attention mechanisms, three-dimensional scenes, aerial scenes, edge computing, etc. Finally, the characteristics of the YOLO series are summarized and the possible improvement ideas and research trends are analyzed in combination with the latest literature.
- Object detection,
- YOLO,
- Deep learning,
- Convolutional Neural Network (CNN)

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References(64)

References

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