Helmet detection based on video monitoring often encounters challenges such as large memory requirements, high complexity of models and difficulty in applying on ubiquitous terminals on construction sites. To address the above problems, a lightweight-aware safety helmet detection method for multi-platforms based on mixed MobileNetv2-YOLOv4 is proposed. First, a lightweight MobileNetv2 backbone is introduced to reduce the number of parameters. Then, a simple model pipeline is achieved by combining the lightweight backbone and widely used YOLOv4 detection head with Feature Pyramid Network (FPN). Also, modifications are made to both the backbone and detection head for a better trade-off between lightweight and performance (speed and accuracy). The whole model is coded under the Darknet framework in the C language and is successfully deployed on a mobile device. Finally, massive experiments and a case study in the real condition are performed to validate the feasibility of the model. The model is lightweight with only 10.4 MB and 3.39 BFLOPs while reaching comparable performance against other detection methods. The model reaches a mAP of 73.1% on the PASCAL VOC 2007 + 2012 dataset and 89.3% on the GDUT-HWD dataset, outperforming YOLOv4, YOLOv5 and Faster R-CNN in speed and model size. The results show that the model has the universality to be implemented in various scenes and the flexibility to be deployed on various devices including workstations, normal PCs and mobile devices. And the safety management level on construction sites is also improved through the successful deployment.