Video Stream Safety Helmet Recognition Method based on improved Faster R-CNN

In order to improve the recognition accuracy and speed of safety helmets, a video stream safety helmet recognition method based on improved Faster R-CNN is proposed. By using the weighted average method to greyscale video stream images, the processing flow is simplified, and median filtering is used to remove noise and improve image quality. The improved Faster R-CNN significantly improves the accuracy and robustness of object recognition by combining the PISA module and deformable convolutional layer (DCN). The PISA module optimises sample weight allocation through importance-based sample weights (ISR) and CARL loss functions. DCN enhances the accuracy of feature extraction by learning the offset of receptive field sampling points. The experimental results show that the safety helmet recognition accuracy of our method reaches 97%, and the recognition speed reaches 30FPS. The improved Faster R-CNN can effectively address the challenge of improving target recognition accuracy in complex scenarios.