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The accurate detection of sperms and impurities is a very challenging task,facing problems such as the small size of targets, indefinite targetmorphologies, low contrast and resolution of the video, and similarity ofsperms and impurities. So far, the detection of sperms and impurities stilllargely relies on the traditional image processing and detection techniqueswhich only yield limited performance and often require manual intervention inthe detection process, therefore unfavorably escalating the time cost andinjecting the subjective bias into the analysis. Encouraged by the successes ofdeep learning methods in numerous object detection tasks, here we report a deeplearning model based on Double Branch Feature Extraction Network (DBFEN) andCross-conjugate Feature Pyramid Networks (CCFPN).DBFEN is designed to extractvisual features from tiny objects with a double branch structure, and CCFPN isfurther introduced to fuse the features extracted by DBFEN to enhance thedescription of position and high-level semantic information. Our work is thepioneer of introducing deep learning approaches to the detection of sperms andimpurities. Experiments show that the highest AP50 of the sperm and impuritydetection is 91.13% and 59.64%, which lead its competitors by a substantialmargin and establish new state-of-the-art results in this problem.

ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos