Morphology detection of converter mouth based on Mask-RCNN
DAI Zhang-jie1, HUANG Cheng-yong1,2, LIU Wei1, XIA Jian-chao3, YANG Shu-feng1, LI Jing-she1
1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China; 2. Equipment Department, Shanghai Meishan Iron and Steel Co., Ltd., Nanjing 210039, Jiangsu, China; 3. Steelmaking Plant, Shanghai Meishan Iron and Steel Co., Ltd., Nanjing 210039, Jiangsu, China
Abstract：With the further improvement of the automation requirements of the steelmaking process, it is of great significance to realize the automatic feeding of the process. Because the converter smelting is often accompanied by the phenomenon of slag overflow, the furnace mouth is easy to form nodules, resulting in the reduction of the inner diameter, which affects the next furnace feeding process. At present, the site mainly observes the morphology of the furnace mouth manually to determine whether it is necessary to repair the furnace mouth. This traditional manual detection is inefficient, and the detection results are unstable due to factors such as the light intensity in the furnace and the subjective judgment of the technicians. With the rapid development of deep learning methods in the field of artificial intelligence and its important role in various industries, a converter mouth shape detection method based on Mask-RCNN is proposed. Based on the output results of the network model, the method calculates the perimeter and area of the furnace mouth contour through image processing, and combines the least squares circle fitting method (LSCM) to characterize the morphology more reasonably and quantitatively through the roundness index. The experiment shows that with the continuous smelting, the area and perimeter of the furnace mouth will continue to decrease, and the corresponding roundness error value will continue to increase, which indicates that the morphology of the furnace mouth will continue to change due to the phenomenon of slag adhesion. The automatic detection method can perform real-time stable detection on the furnace mouth with different rotation angles when feeding or slag discharge. The recognition rate is as high as 99%, which has high detection performance. Compared with manual detection, it has the advantages of stability, accuracy and efficiency, and greatly improves the process stability.
戴张杰, 黄成永, 刘威, 夏建超, 杨树峰, 李京社. 基于Mask-RCNN的转炉炉口形貌检测[J]. 钢铁, 2023, 58(3): 73-78.
DAI Zhang-jie, HUANG Cheng-yong, LIU Wei, XIA Jian-chao, YANG Shu-feng, LI Jing-she. Morphology detection of converter mouth based on Mask-RCNN[J]. Iron and Steel, 2023, 58(3): 73-78.
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2023, Vol. 58 
