el 22(23): e5

Research Article

Review on One-Stage Object Detection Based on Deep Learning

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  • @ARTICLE{10.4108/eai.9-6-2022.174181,
        author={Hang Zhang and Rayan S Cloutier},
        title={Review on One-Stage Object Detection Based on Deep Learning},
        journal={EAI Endorsed Transactions on e-Learning},
        volume={7},
        number={23},
        publisher={EAI},
        journal_a={EL},
        year={2022},
        month={6},
        keywords={Object Detection, IoT, Deep learning, Computer Vision},
        doi={10.4108/eai.9-6-2022.174181}
    }
    
  • Hang Zhang
    Rayan S Cloutier
    Year: 2022
    Review on One-Stage Object Detection Based on Deep Learning
    EL
    EAI
    DOI: 10.4108/eai.9-6-2022.174181
Hang Zhang1,*, Rayan S Cloutier2
  • 1: School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, P R China
  • 2: Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
*Contact email: zh@home.hpu.edu.cn

Abstract

As a popular research direction in computer vision, deep learning technology has promoted breakthroughs in the field of object detection. In recent years, the combination of object detection and the Internet of Things (IoT) has been widely used in the fields of face recognition, pedestrian detection, unmanned driving, and customs detection. With the development of object detection, two different detection algorithms, one-stage, and two-stage have gradually formed. This paper mainly introduces the one-stage object detection algorithm. Firstly, the development process of the convolutional neural network is briefly reviewed, Then, the current mainstream one-stage object detection model is summarized. Based on YOLOv1, it is continuously optimized, and the improvements and shortcomings are summarized in detail. Finally, a summary is made based on the difficulties and challenges of one-stage object detection algorithms.