Seatbelt Recognition Method Based on Convolutional Attention Mechanism

In order to solve the problem of low accuracy of the driver's seat-belt identification and neglecting co-pilot, a method of driver's seat-belt identification based on deep convolutional channel modeling is improved. Firstly, the characteristic parameters of redundant channels are reduced by means of deep convolutional feature channel compression. Then, the feature weight of the deep convolutional channel is re-calibrated to improve the attention weight of the feature of the safety belt and suppress the expression of the interference feature. Finally, the central loss function is used to assist feature training of the model, so as to reduce the difference of feature distribution within the class and make the learned features easier to be classified and recognized. The experimental results show that, compared with other algorithms, the improved deep convolution driver seat belt identification model in this paper improves the recognition accuracy, recall rate, and F value by 6.9%, 4.32 and 5.5% respectively, and greatly improves the accuracy and practicability.