The microscopic image has the characteristics of complex background and overlapping cells. Due to the technical limitations, traditional image processing methods cannot accurately complete the real-time recognition task. To address the above-mentioned problems, we propose an automatic detection method for microscopic images using attention mechanism. This method improves the original DETR architecture by introducing a split-transform-merge mechanism, which reduces the dimensionality of input features and trains multiple groups of convolution kernels for feature extraction, thereby effectively improving the model's feature extraction ability for the targets and increasing the accuracy of model detection rate. The experimental results show that the mAP of the improved model was 96.3%, which is 10% higher than that of the original model DETR. Meanwhile, the proposed method has superior detection capabilities for scenarios such as cell overlap, adhesion, and complex background. Moreover, the detection time for each leucorrhea image was about 88.8 ms, which can satisfy the requirement of real-time microscopy examination.