Classification of Sintered Flame Images Based on Improved Clustering Algorithm

The flame image at the tail section of the sintering machine can reflect the state of the sintering endpoint directly and effectively. It is feasible and practical in engineering to utilize the effective information in the flame image to classify the state of the sintering endpoint. Therefore, this paper proposes a classification algorithm based on K-means with the image color features to classify the sintering states of the flame at the tail section of the sintering machine. First, 90 flame images were preprocessed. The section images with 320 m(2) that were collected by the sintering machine were cut uniformly in the red fire area according to the resolution of 3024 X 1700 pixels. Then, the core areas were extracted and sintered. The K-mean segmentation of the clipped image and the comparison of the segmentation images with K values of 2, 3, and 4 show that the segmentation results when K = 3 can be used to segment the red fire area of the flame more accurately. Second, the color features of the red fire area are further extracted to obtain the final red fire target area segmentation image, since there are still other nonred fire areas in the segmented image. Finally, the geometric features of the extracted target image were taken as the dataset, and a fuzzy C-means (FCM) algorithm was used to classify the sintering end state. The classification effect of the proposed flame image classification method improves more than that of the traditional FCM algorithm.