Adhesive tobacco shreds recognition method based on improved Mask R-CNN model
To achieve accurate identification and efficient segmentation of adhesive tobacco shreds,a method for adhesive tobacco shreds recognition based on an improved Mask R-CNN(Mask Region-based Convolutional Neural Network)model was proposed.Firstly,adhesive tobacco shreds images were collected,and the dataset was aug-mented through image enhancement operations to expand it to the required sample size for training the model.Sec-ondly,edge feature extraction and fractal feature extraction were performed on the adhesive tobacco shreds images in the training set based on Mask R-CNN,resulting in clearer and more continuous image edge features and texture feature information.Subsequently,the original features,edge features,and fractal features were fused to fully uti-lize features at different levels and enrich low-level features.Finally,by introducing a hybrid attention mechanism that focused on both channel and spatial dimensions of feature maps,the efficiency and accuracy of tobacco shred recognition were improved.Experimental results showed that the mean intersectionover union(Avg.MIoU)of the recognition method based on the improved Mask R-CNN model was 85.29％,and the mean class pixel accuracy(Avg.MPA)was 84.33％,under different adhesion conditions enabling precise identification of tobacco shreds and outperforming the original Mask R-CNN and DeepLabV3+models.This method could rapidly and accurately identify and segment adhesive tobacco shreds,providing technical support for subsequent tobacco shred width detection.
万方数据
维普
