YOLOv5-DCN: An Effective Improvement Based on YOLOv5 Detector
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.
Authors
- Ali Khatibi (ORCID: https://orcid.org/0000-0002-2531-7720)
- Jacquline Tham (ORCID: https://orcid.org/0000-0003-0966-2425)
- Xiaoxia Qi (ORCID: https://orcid.org/0000-0002-7358-5260)
- Long Cheng (ORCID: https://orcid.org/0000-0001-7565-8788)
- Md Gapar Md Johar
Publication Details
- Journal
- International Journal of Computer Science and Information Technology
- Published
- 2024-11-24
- DOI
- https://doi.org/10.62051/ijcsit.v4n3.04
- Primary Topic
- Ga2O3 and related materials
- Type
- article
- Field-Weighted Citation Impact
- 0.00