SpermYOLO：一种基于YOLO的协同检测器，用于显微图像中精子与杂质的准确高效检测
SpermYOLO: A Coordinated YOLO-Based Detector for Accurate and Efficient Sperm and Impurity Detection in Microscopic Images
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中文总结 AI 辅助
针对显微图像中精子检测的挑战，本文提出SpermYOLO，一种基于YOLOv11的紧凑协同检测框架，通过四项架构改进在SVIA和SDTB基准上显著提升检测精度，同时保持轻量级模型。
中文摘要 AI 辅助
准确的精子检测对于计算机辅助精液分析至关重要，然而由于精子在显微图像中分布密集、存在视觉上相似的伪影以及类精子杂质，这一任务仍具挑战性。本文提出SpermYOLO，一种协同且紧凑的、源自YOLOv11的框架，用于显微图像中精子与杂质的联合检测。SpermYOLO引入了四项架构改进：C3k2-IDB用于通道级判别性特征提取，D2SEM用于空间-光谱语义增强，MFM用于自适应多尺度特征融合，以及DESD Head用于细节增强的共享预测。在SVIA精液显微成像基准上的实验表明，SpermYOLO实现了97.2%的精子AP和75.4%的杂质AP，优于通用检测器、专用精子检测模型及改进的YOLO变体。与基线模型相比，SpermYOLO将精子AP、杂质AP、mAP50和mAP50:95分别提升了1.6、10.0、5.8和2.7个百分点，同时保持了轻量级的模型规模。在SDTB睾丸活检显微基准上的跨场景评估显示，SpermYOLO在极小精子目标和复杂组织背景下依然有效，分别达到了74.8%和31.2%的最高mAP50和mAP50:95。消融研究和定性分析进一步支持了这些改进，证明了所提出模块的贡献，并显示出比基线模型更集中的特征响应模式。这些发现表明，SpermYOLO是在具有挑战性的显微成像场景中进行精子检测的一种有效且高效的方法。
英文摘要
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.
发表机构
- Beijing University of Posts and Telecommunications（北京邮电大学）
- Zhongyuan University of Technology（中原工学院）
机构由 AI 辅助整理，请以论文原文为准。
