Abstract: Aiming at the problems of large changes in target shape and scale, which are prone to missed detection and false detection in safety helmet detection, a safety helmet detection algorithm based on improved Cascade Region-based Convolutional Neural Networks(Cascade R-CNN) is proposed. Firstly, ResNet50 is improved to form D-ResNet50, which can increase the perceptual field by using the feature of deformable convolution with only a small increase of parameters, and reshape the C2 to C5 convolutional layers of the feature extraction network to improve the network's adaptability to target geometric transformation and feature extraction capability. Secondly, D-ResNet50 is introduced into Cascade R-CNN as the backbone network to form a cascade target detector. Convolutional Neural Networks(CNN) to form a cascade target detector, resampling positive and negative samples at each stage to suppress the false detection problem. Thirdly, the recursive feature pyramid is improved to perform multi-scale feature fusion more efficiently, and the features are processed twice based on feedback information to enhance feature representation and improve the classification and localization ability of the network. Finally, post-processing is performed using Soft-Non-Maximum Suppression(Soft-NMS) to further solve the problem of missed detection. The proposed method improves the AP value on Hard hat workers dataset by 3.5% compared to the detection baseline, and by 4.7%, 5.9%, 2.3%, etc. compared to the advanced algorithms such as Sparse R-CNN, TridentNet, and VFnet, respectively.