To solve the problem of low recognition rates on helmet dataset， this paper proposes a detection method based on an improved CenterNet network structure. To tackle the problem of poor prediction results in the multi-class classification of CenterNet， this paper attempts to improve the loss function. Therefore， Focal-Mse-One loss and Focal-Mse-Guss loss are proposed and compared with the original Focal loss； Aiming at the problem of low reusability of feature map in the reasoning process of CenterNet， ASFF and DASFF structures are proposed and compared. The experimental results show that the reasoning speed can reach 20.78 frames on GeForce GTX 1050 graphics card. When the IOU is 0.5， the mAP can reach 81.43% on the helmet dataset， which is 3.63% higher than the original CenterNet’s mAP. The improved method proposed in this paper can significantly improve the detec tion accuracy of safety helmet without a significant increase in reasoning time.