YOLOv5-DCN: An Effective Improvement Based on YOLOv5 Detector
DOI:
https://doi.org/10.62051/ijcsit.v4n3.04Keywords:
Urine Sediment, YOLOv5, Deformable ConvolutionAbstract
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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[1] Zou Z, Shi Z, Guo Y, et al. Object Detection in 20 Years: A Survey [J]. 2019.
[2] Redmon J, Divvala S, Girshick R, et al. You Only Look Once: Unified, Real-Time Object Detection[C]//Computer Vision & Pattern Recognition. IEEE, 2016.
[3] Goswami D, Aggrawal H O, Gupta R, et al. Urine Microscopic Image Dataset [J]. 2021.
[4] Dai J, Qi H, Xiong Y, et al. Deformable Convolutional Networks [J]. IEEE, 2017.
[5] Ji Q, Jiang Y, Qu Z W L. An Image Recognition Method for Urine Sediment Based on Semi-supervised Learning [J]. Innovation and research in biomedical engineering: IRBM, 2023, 44(2):100739.1-100739.9.
[6] Terven J, Cordova-Esparza D M, Romero-Gonzalez J A. A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS [J]. Machine Learning and Knowledge Extraction, 2023, 5(4):1680-1716.
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Copyright (c) 2024 International Journal of Computer Science and Information Technology
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