The pulverized coal injection (PCI) blockage detection is critical to the stable operation of blast furnace. In recent years, tuyere cameras have been widely applied, which provides a channel to detect the PCI blockage. However, the visual impression of images strongly varies between different raceways, it requires detection method should be robust and convenient to fine-tune for different blast furnace images. This paper presents an intelligent image-based method to detect the PCI blockage. An adaptive image preprocessing technique combining de-noising algorithm and image enhancement algorithm is applied to remove image noise and improve image quality, laying the foundation for subsequent work. The fitting ellipse based on Hough transform is used to locate the tuyere region, which can separate the tuyere region from the background. The adaptive threshold segmentation algorithm combining Otsu and Bernsen is used to obtain binarized image. However, it is difficult to obtain the pulverized coal cloud only by binarization due to the similarity between pulverized coal cloud and lance in gray-level. The multi-scale fully convolutional network (FCN) based on deep learning is investigated to detect the lance region, and pulverized coal cloud can be extracted by removing lance in binarized image. The flow rate of PCI can be characterized by the extracted area information to some extent, which can be used to detect PCI blockage. Extensive videos captured from real production lines are used to evaluate the detection method. The experiment results show that the method can accurately detect the PCI blockage.