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International Journal of Computer Science and Information Technology
Article . 2024 . Peer-reviewed
License: CC BY NC
Data sources: Crossref
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YOLOv5-DCN: An Effective Improvement Based on YOLOv5 Detector

descriptionPublicationkeyboard_double_arrow_right Article 24 Nov 2024Publisher:Warwick Evans PublishingJournal:International Journal of Computer Science and Information Technology, volume 4, pages 31-35 (issn: 3005-9682, eissn: 3005-7140, Copyright policy )
Authors: Xiaoxia Qi; Md Gapar Md Johar; Ali Khatibi; Jacquline Tham; Long Cheng;

doi: 10.62051/ijcsit.v4n3.04

YOLOv5-DCN: An Effective Improvement Based on YOLOv5 Detector

Abstract

Urine sediment detection is of great significance for the diagnosis and monitoring of kidney diseases, urinary tract infections, stones, etc. This study aims to propose fast, high-precision and lightweight Urinary particles detection model based on YOLOv5, deformable convolution, and evaluate its performance in Urinary particles detection tasks.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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