Improving Helmet-Wearing Detection with Human Detection

The helmet-wearing detection task requires detecting whether a person is wearing a helmet or not from a given image or video. Existing studies using deep learning share the problem that the detection performance degrades when the resolution of the target person becomes low. In addition, the training cost of neural network models and the labor cost of data collection are required to improve the performance. To this end, we propose to improve the performance of helmet-wearing detection using a pre-trained off-the-shelf human detection model without additional training cost, which is simple yet effective. Specifically, the helmet is re-identified using the positional relationship between the results of human detection and helmet-wearing detection, which is based on the observation that a helmet should be within the bounding box of a person. In the experiment, we confirm that, especially at low resolution, our method can significantly improve the recall of the model and further improve the F1 score.