Detection and segmentation of iron ore green pellets in images using lightweight U-net deep learning network

In steel manufacturing industry, powdered iron ore is agglomerated in a pelletizing disk to form iron ore green pellets. The agglomeration process is usually monitored using a camera. As pellet size distribution is one of the major measures of product quality monitoring, pellets detection and segmentation from the image are the key steps to determine the pellet size. Traditional image processing algorithms are not only challenged by the complicated constitution of pellets, sediment and residuals in the image, but also by the harsh and unbalanced light reflection on the pellet centrum area and the background which results in tedious parameter adjustment work and pool performance. To solve these problems, we design a lightweight U-net deep learning network to automatically detect pellets from images and to obtain the probability maps of pellet contours. Compared to classic U-net, the proposed network has fewer parameters and introduces batch normalization layers, which greatly reduces the computing time and improves generalization ability of the network. A concentric circle model is then used to separate clumped contours of the pellets, and the pellets shapes are detected via ellipse fitting. The proposed method is verified using images captured from an industrial pelletizing disk, and its performance is compared with traditional methods and the classic U-net. Results show that the proposed method achieves better segmentation performance in DICE and ROC indexes and shows good robustness to uneven illumination. Tests on temporal image sequences demonstrate that the proposed method is effective in monitoring the pellet size distribution and the pellet shape as well. Results of this work have potential usage in online detection of iron ore green pellets and other types of particles.