ACTIVE：一种用于显微视频中精子和杂质检测的深度模型
ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos
摘要：
精子和杂质的准确检测是一项非常具有挑战性的任务，面临着目标尺寸小、目标形态不确定、视频对比度和分辨率低、精子和杂质相似等问题。目前，精子和杂质的检测仍主要依赖于传统的图像处理和检测技术，其性能有限，且检测过程中往往需要人工干预，这不仅增加了时间成本，而且给分析注入了主观偏见。受深度学习方法在众多目标检测任务中取得成功的鼓舞，本文报告了一种基于双分支特征提取网络（DBFEN）和交叉共轭特征金字塔网络（CCFPN）的深度学习模型。DBFEN旨在从具有双分支结构的微小目标中提取视觉特征，进一步引入CCFPN融合DBFEN提取的特征，增强了对位置和高层语义信息的描述。我们的工作是将深度学习方法引入精子和杂质检测的先驱。实验表明，该方法对精子和杂质检测的最高AP50分别为91.13%和59.64%，大幅领先竞争对手，在该问题上建立了新的先进成果。
英文摘要：
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities. So far, the detection of sperms and impurities still largely relies on the traditional image processing and detection techniques which only yield limited performance and often require manual intervention in the detection process, therefore unfavorably escalating the time cost and injecting the subjective bias into the analysis. Encouraged by the successes of deep learning methods in numerous object detection tasks, here we report a deep learning model based on Double Branch Feature Extraction Network (DBFEN) and Cross-conjugate Feature Pyramid Networks (CCFPN).DBFEN is designed to extract visual features from tiny objects with a double branch structure, and CCFPN is further introduced to fuse the features extracted by DBFEN to enhance the description of position and high-level semantic information. Our work is the pioneer of introducing deep learning approaches to the detection of sperms and impurities. Experiments show that the highest AP50 of the sperm and impurity detection is 91.13% and 59.64%, which lead its competitors by a substantial margin and establish new state-of-the-art results in this problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
