Research Article
Review on One-Stage Object Detection Based on Deep Learning
@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
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.
Copyright © 2022 Hang Zhang et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
