Improved dense residual network with the coordinate and pixel attention mechanisms for helmet detection

Helmet detection in road surveillance images has become increasingly important with the increasing number of accidents involving two-wheeled electric vehicles and motorcycles. However, small detection targets and complex road environments make traditional helmet detection methods difficult. In this study, we propose an intelligent helmet detection model based on convolutional neural networks. To accurately capture the location of the helmet, we introduce the coordinate attention to obtain position information in the model. We thereafter introduce the pixel attention to enhance interpixel correlation and pixel-level feature filtering for the input images. These two attention mechanisms are combined to design the CPA module, and multi-CPA groups are constructed in a densely connected manner to obtain improved CPAG dense blocks. The proposed dual-attention mechanism effectively enhanced the weight of useful information and suppressed useless information. A dense block can improve the feature extraction ability and avoid information loss in the network. The CPAG dense block is inserted into the convolutional network model to obtain CPAG-Net as the detection network. To complete the system, we added a localization network to obtain the upper part of the rider. The localization network is accomplished using an improved YOLOv5s model in which we introduce an efficient channel attention mechanism to improve the localization ability for small targets. We compared the performance of the proposed method with those of several other methods. The results indicate that the proposed method is more robust than the other methods and has a higher accuracy for helmet detection in road surveillance images.