An improved multi-vehicle door classification algorithm based on ResNet
Aiming at the low-accuracy problem of the multi-model door classification algorithm in the flexible welding production line,this study designs a multi-model door classification algorithm based on the improved ResNet18 model.In order to improve the feature extraction and expression ability of the model,the algorithm model introduces a channel-enhanced attention module and a dense residual module.The channel-enhanced attention module improves the model's ability to pay attention to key information by adaptively strengthening the important feature channels,while the dense residual module effectively mitigates the gradient vanishing problem by connecting the features of multiple layers and promotes the flow and reuse of information,which in turn enhances the model training effect and accuracy.Combining these two modules,the algorithmic model significantly improves the recognition accuracy of doors of different car models and its generalization ability.After a series of experimental validations,the improved ResNet18 model achieves 99.01%in Top 1 accuracy,which is a 1.8%improvement over the original ResNet18 model.
machine visionimage recognitiondeep learningclassification model