Universal Village (UV) is a new concept proposed by MIT’s Universal Village Program, which envisions an ideal future society that prioritizes environmental and ecosystem preservation while fulfilling human needs, thereby ensuring sustainable wellbeing for society’s residents. Marine ecological conservation is an integral part of the Global Village plan, and algae are essential marine biological resources. The organic matter and accumulated energy they produce serve as the foundation for the survival and development of the entire marine biological community. With the rapid development of computer vision technology, there is now the possibility of combining advanced object detection algorithms with traditional algae detection methods. In this work, we leveraged Swin Transformer and Covnext as feature extractors and Cascade RCNN as the main network to detect and classify eight types of microalgae in microscope images. We also employed Weighted Boxes Fusion in post-processing to improve detection accuracy and model generalization.