Image Segmentation Based on Improved U-Net Model for Beneficiation Product Zone in Dry Magnetic Separation
Aiming at uncertainty of beneficiation product zone in dry magnetic separation process,an image segmentation method based on an improved U-Net model was proposed by employing machine vision.In this improved model,convolutional block attention module(CBAM)is utilized to enhance the recognition and attention of the network for target areas,which is beneficial to the segmentation of target objects under complex backgrounds;depth-wise separable convolution is adopted to reduce computational complexity while maintaining accuracy,providing strong support for obtaining high-resolution images of beneficiation product zone.Thus,this model can be applied in magnetic separation and also improve network performance.It is found that this improved model can bring segmentation accuracy up to 92.28%,and also is superior to classic U-Net,DeepLabV3+and PSPNet models in terms of contour extraction completeness and denoising capabilities.
万方数据
维普
