  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.
