Efficient-Unet: Intelligent identification of abrasive grain on the entire surface of monolayer brazing wheel based on encoder–decoder network

Measuring and extracting abrasive grains on the entire surface of monolayer brazing grinding wheels to analyze the distribution of abrasive grains are of great significance to grinding research and grinding wheel manufacture. It is not easy to carry out the work with traditional methods. In this paper, a linear CCD is used to acquire the entire grinding wheel surface image, and an improved encoder–decoder network based on Efficientnet, ASPP, and Skip Connections is designed to promote the accuracy and speed of abrasive grains prediction. Based on dataset creation, transfer learning, and hyper-parameter testing, 91.21% abrasive grain semantic segmentation accuracy was finally obtained. If we focus on whether the abrasive grains are recognized without considering semantic errors, an accuracy rate of 99.6% is obtained. The method can provide basic data for grinding mechanism research and abrasive tool manufacturing.