Research Of Vehicle Recognition Based On Octave Convolution And Self-attention Mechanism

With the growth of image information in data,the analysis and information extraction of images and videos is becoming more and more important.How to accurately analyze and classify images and improve the accuracy of image recognition is one of the main research directions of researchers at this stage.Artificial intelligence technology,the rapid development of machine learning and the proposal of convolutional neural networks have made new progress in the field of image classification.In order to better improve the accuracy of image recognition,this paper improves the convolutional layer in the convolutional neural network and adds a channel domain self-attention mechanism.The main contributions of this paper are.:(1)In order to improve the recognition accuracy of the convolutional neural network,an OCM(Octave Convention Multilayer Perceptron)optimized convolutional layer and OCM neural network based on the Octave convolutional layer structure is proposed.The addition of a multilayer perceptron to the OCM convolutional layer structure improves the nonlinear expression of the convolutional layer compared to traditional convolutional layers.OCM neural networks based on OCM convolutional layer have larger and more powerful sensory field and contextual information capture capabilities than traditional neural networks.The experimental results show that on the CIFAR-10 and CIFAR-100 datasets,the OCM convolutional neural network proposed in this paper has improved the image recognition accuracy by 5.5%and 9.8%,respectively,compared to the conventional model.(2)In order to solve the gradient disappearance problem that occurs with the activation function of the Excitation operation of the channel domain attention mechanism,a new activation function is used,with a penalty factor added to the function to enrich the nonlinearity between channels,suppress the unhelpful information between channels,avoid the gradient death problem,and better restore the complex correlation between channels.The experimental results showed that the image recognition accuracy was improved by1.3% and 1.4% on the CIFAR-10 and CIFAR-100 datasets compared to the conventional model.(3)In the community intelligent vehicle management system,by applying the OCM neural network proposed in this paper.It identifies car model information and restricts some vehicles from entering the community,improving the park environment and ensuring the safety of community owners.The vehicleidentification module is realized,the vehicle identification time is fast,the accuracy is high.And the identification category results and probability can be displayed intuitively.