SpermYOLO: A Coordinated YOLO-Based Detector for Accurate and Efficient Sperm and Impurity Detection in Microscopic Images

📅 2026-09-13
📈 Citations: 0
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🤖 AI Summary
针对微观图像中精子和杂质检测难题,提出基于YOLO改进的SpermYOLO框架,通过四项架构改进实现高效准确检测。
📝 Abstract
Accurate sperm detection is essential for computer-assisted semen analysis, yet it remains challenging in microscopic images due to dense distributions, visually similar artifacts, and sperm-like impurities. In this paper, we propose SpermYOLO, a coordinated and compact YOLOv11-derived framework for joint sperm and impurity detection in microscopic images. SpermYOLO introduces four architectural improvements: C3k2-IDB for channel-wise discriminative feature extraction, D2SEM for spatial--spectral semantic enhancement, MFM for adaptive multi-scale feature fusion, and the DESD Head for detail-enhanced shared prediction. Experiments on the SVIA semen microscopic imaging benchmark show that SpermYOLO achieves 97.2\% sperm AP and 75.4\% impurity AP, outperforming generic detectors, dedicated sperm detection models, and improved YOLO variants. Compared with the baseline model, SpermYOLO improves sperm AP, impurity AP, $\mathrm{mAP}_{50}$, and $\mathrm{mAP}_{50:95}$ by 1.6, 10.0, 5.8, and 2.7 percentage points, respectively, while preserving a lightweight model scale. Cross-scene evaluation on the SDTB testicular-biopsy microscopy benchmark shows that SpermYOLO remains effective with extremely small sperm targets and complex tissue backgrounds, achieving the highest $\mathrm{mAP}_{50}$ and $\mathrm{mAP}_{50:95}$ of 74.8\% and 31.2\%, respectively. Ablation studies and qualitative analyses further support these improvements by demonstrating the contributions of the proposed modules and showing more focused feature response patterns than the baseline model. These findings suggest that SpermYOLO is an effective and efficient approach for sperm detection in challenging microscopic imaging scenarios.
Problem

Research questions and friction points this paper is trying to address.

sperm detection
microscopic images
impurities
Innovation

Methods, ideas, or system contributions that make the work stand out.

C3k2-IDB
D2SEM
MFM
DESD Head
joint sperm and impurity detection
S
Shengqi Chen
School of Electronic Engineering, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing 100876, China
Zilin Wang
Zilin Wang
University of Oxford
Deep Reinforcement LearningAutonomous Driving
X
Xingyu Pan
School of Electronic Engineering, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing 100876, China
W
Wenting Yu
School of Information and Communication Engineering, Zhongyuan University of Technology, 41 Zhongyuan Middle Road, Zhengzhou, Henan Province 450007, China
Pengchao Deng
Pengchao Deng
Xi'an Jiaotong University
Computer VsionFace anti-spoofing
G
Guohua Wu
School of Electronic Engineering, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing 100876, China