Microalgae microscopic image target detection technology is one of the important research directions in fields such as biology and environmental monitoring. The dataset of microalgae images captured by electron microscope exhibits a long-tail data issue. Traditional methods for microalgae detection are notoriously labor-intensive, time-consuming, and heavily influenced by operator expertise. In this context, combining methods to address the long-tail distribution, this paper proposes a target detection algorithm called DDM-YOLO, which combines delayed resampling and knowledge distillation. The approach involves data augmentation for microalgae microscopic images and utilizes delayed resampling for long-tail data. In the second stage, reverse resampling is applied to focus on the challenging minority class samples, thereby enhancing the performance of target detection. Additionally, a lightweight target detection network architecture is designed, and knowledge distillation is employed to reduce model complexity and computational requirements. Experimental outcomes reveal that the DDM-YOLO algorithm achieves an mAP@0.5/% of 77.1%, surpassing the YOLOv5s algorithm by a notable 6.1%. The model parameter size is 3.88 megabytes, a significant 45.4% decrease. This proposed method significantly enhances performance on microalgae microscopic image data and efficiently performs target detection under resource-constrained conditions, substantially reducing the workload of detection personnel.