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

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, thus unfavorably escalating the time cost and injecting the subjective bias into the analysis. Encouraged by the success of deep learning methods in numerous object detection tasks, here we report a deep learning network: ACTIVE based on Double Branch Feature Extraction Network (DBFEN) and Cross-conjugate Feature Pyramid Network (CCFPN). DBFEN extracts visual features from tiny objects with a double branch structure, and CCFPN fuses the features extracted by DBFEN to enhance the description of the 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 the state-of-the-art results in this problem. Code is available for readers’ free evaluation at https://github.com/anheqiao-neu/ACTIVE.