Research on bamboo defect segmentation and classification based on improved u-net network

In this paper, computer vision technology is used to quickly and accurately identify and classify the surface defects of processed bamboo, which overcomes the low efficiency of manual identification. The datasets consist of 6360 defective bamboo mat images of four categories taken by the author at the same position, which are split at a ratio of 8:2 for training and testing. In this experiment, we improved the U-net to segment the datasets and use VGG16, GoogLeNet and ResNet50 with attention mechanism for classification and comparison. The experimental results show that the accuracy of this method is 5.65% higher than the commonly used neural network method. The highest accuracy rate is 99.2%.