Work

YOLOv5-DCN: An Effective Improvement Based on YOLOv5 Detector

Year: 2024

Type: article

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.

Source: International Journal of Computer Science and Information Technology

DOI: 10.62051/ijcsit.v4n3.04

Authors: Xiaoxia Qi, Md Gapar Md Johar, Ali Khatibi, Jacquline Tham, Long Cheng

Institutions: Management and Science University, Anhui Jianzhu University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4405389130

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