## 1 Introduction
[9] calculated the edge complexity, weighted the sub-interval of the histogram, simplified the weighted histogram, and used the simplified histogram to distinguish the features of the image, which was significantly better than the gray histogram. Image enhancement is a common method of image processing and plays an important role in image processing. [PERSON] et al. [10] added smoothing to fuzzy enhancement to improve the image fuzzy enhancement system. [PERSON] et al. [11] proposed a membership function to extract pixels near the object boundary and adopted the threshold selection algorithm to determine the threshold value so that the gray value of the enhanced image could be more balanced. Although researchers have made great efforts in developing the detection technology of the blast furnace steelmaking image, the automatic detection of blast furnace combustion is still a preliminary application, and there is still much room for improvement in the detection effect. Because of the special structure and complexity of the combustion process, there are many problems to be overcome. First, a burner has only one fixed detection \"window\", the distance between the raceway and the camera is long, resulting in a small field of view of the raceway flame, and the raceway can only be viewed from this one direction. Secondly, not only is the combustion temperature very high, but there is also the interference of the pulverized coal and coke in the raceway; the information obtained from the detection system is largely limited by these factors. In recent years, deep learning has become a research hotspot. It has been widely applied and has achieved remarkable results in image processing, speech recognition, and other fields, especially in image classification and image recognition [12; 13; 14; 15]. In our work, an abnormal detection method of the blast furnace tuyere based on an improved channel attention mechanism and a residual network is proposed to learn the actual abnormal condition of the blast furnace raceway. A monitoring camera is used as a sensor to obtain the image of the blast furnace tuyere. First, the frame is read to obtain the image, and the region of interest of the furnace mouth is intercepted to eliminate the influence of the noise and background. Moreover, the channel attention mechanism is improved; combined with the residual network, the abnormal detection of the blast furnace tuyere is studied. The aim of our methodology is to replace manual detection while greatly improving efficiency and accuracy so as to achieve a set of automatic, efficient, and very accurate intelligent recognition data. There were also some detection errors in the previous manual detection, but now, there are a variety of sensor alarms so that errors can also be dealt with at an early stage, and our method has a very high accuracy rate; even if there is a very small probability of error, it will not have a big impact. Additionally, the model will be gradually applied in the case of semi-manual intervention, and only when there are no major problems will it fully replace manual labor, and a complete early warning system will be developed before the model is applied. The Section 2 of the article concerns the materials and methods and introduces the related materials and the specific structure of the model. The Section 3 is on the results; this section introduces the procedure of the experiment and the discussion of the results. The Section 4 summarizes the method we proposed and the results we obtained.