The fingerprint identification system has been challenged and questioned following the erroneous fingerprint individualization in the Madrid train bombings case. Therefore, the quantitative identification technology based on the statistical law of fingerprint secondary features is now a prevalent research topic, for which the automatic detection and classification of fingerprint second-order minutiae serve as foundations. In this paper, a YOLOv5 based fingerprint second-order minutiae detection method was proposed. First, a fingerprint second-order minutiae dataset was established, which contained 4000 fingerprint images with annotations. The structure of the YOLOv5 network was improved based on the characteristics of small size and dense distribution of fingerprint second-order minutiae. More specifically, the original feature detection layer of 32 times down-sampled large target was deleted, and a new micro-scale detection layer was added. Feature Pyramid Networks (FPN),Pyramid Attention Network (PAN),and Spatial Pyramid Pooling (SPP) structures were used to extract local and global features through multi-scale fusion. Finally, the Squeeze-and-Excitation(SE) channel attentional mechanism was added to effectively enhance the robustness of the model and the detection ability of dense small targets. The experimental results reveal that compared with the original model, the mean average precision(mAP0.5) value of the improved YOLOv5s_FI model increases from 93.0% to 97.4% under the condition that the detection speed is basically unchanged, and the weight of the improved YOLOv5s_FI model is reduced by three quarters.