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The detection of tiny objects in microscopic videos is a problematic point,especially in large-scale experiments. For tiny objects (such as sperms) inmicroscopic videos, current detection methods face challenges in fuzzy,irregular, and precise positioning of objects. In contrast, we present aconvolutional neural network for tiny object detection (TOD-CNN) with anunderlying data set of high-quality sperm microscopic videos (111 videos, $>$278,000 annotated objects), and a graphical user interface (GUI) is designed toemploy and test the proposed model effectively. TOD-CNN is highly accurate,achieving $85.60\%$ AP$_{50}$ in the task of real-time sperm detection inmicroscopic videos. To demonstrate the importance of sperm detection technologyin sperm quality analysis, we carry out relevant sperm quality evaluationmetrics and compare them with the diagnosis results from medical doctors.

TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos