research-article

Review of Target Detection Technology based on Deep Learning

Authors:

Xinjian WuAuthors Info & Claims

CCEAI '21: Proceedings of the 5th International Conference on Control Engineering and Artificial Intelligence

Pages 132 - 135

https://doi.org/10.1145/3448218.3448234

Published: 15 February 2021 Publication History

Abstract

Target detection is one of the most important contents in computer vision, which has been widely and effectively applied in production, daily life, and military. The target detection technology based on deep learning has gone far beyond the traditional target detection technology with high autonomy, accuracy and sensitivity. Based on convolutional neural network, it mainly develops two-stage detection algorithm and one-stage detection algorithm. The two-stage detection algorithms mainly include RCNN, SPP-Net, Fast-RCNN, Faster-RCNN, etc., and the one-stage detection algorithms mainly include Yolo, SSD, RetinaNet, etc. Deep learning framework is an important tool to implements target detection algorithm. Current mainstream deep learning frameworks include PyTorch, TensorFlow, PaddlePaddle, keras, etc.

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CCEAI '21: Proceedings of the 5th International Conference on Control Engineering and Artificial Intelligence

January 2021

165 pages

ISBN:9781450388870

DOI:10.1145/3448218

Editor:
Dan Zhang

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 February 2021

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CCEAI 2021

CCEAI 2021: 5th International Conference on Control Engineering and Artificial Intelligence

January 14 - 16, 2021

Sanya, China

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https://doi.org/10.3390/rs16091589
Fang CLi CYang PKong SHan YHuang XNiu J(2024)Enhancing Livestock Detection: An Efficient Model Based on YOLOv8Applied Sciences10.3390/app1411480914:11(4809)Online publication date: 2-Jun-2024
https://doi.org/10.3390/app14114809
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