论文标题
Tod-CNN：一个有效的卷积神经网络，用于精子视频中的微小对象检测
TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos
论文作者
论文摘要
微观视频中微小物体的检测是一个问题，尤其是在大规模实验中。对于微观视频中的微小对象（例如精子），当前的检测方法面临模糊，不规则和精确定位的挑战。相比之下，我们提出了一个用于微小对象检测的卷积神经网络（TOD-CNN），其基础数据集的高质量精子显微镜视频（111个视频，$> $ 278,000带注释的对象）以及图形用户界面（GUI）旨在采用和测试提议模型。 Tod-CNN非常准确，在微观视频中实时精子检测任务中，$ 85.60 \％$ ap $ _ {50} $。为了证明精子检测技术在精子质量分析中的重要性，我们进行了相关的精子质量评估指标，并将其与医生的诊断结果进行比较。
The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments. For tiny objects (such as sperms) in microscopic videos, current detection methods face challenges in fuzzy, irregular, and precise positioning of objects. In contrast, we present a convolutional neural network for tiny object detection (TOD-CNN) with an underlying data set of high-quality sperm microscopic videos (111 videos, $>$ 278,000 annotated objects), and a graphical user interface (GUI) is designed to employ and test the proposed model effectively. TOD-CNN is highly accurate, achieving $85.60\%$ AP$_{50}$ in the task of real-time sperm detection in microscopic videos. To demonstrate the importance of sperm detection technology in sperm quality analysis, we carry out relevant sperm quality evaluation metrics and compare them with the diagnosis results from medical doctors.
