LAUSR

Real-time and accurate measurement of blast furnace (BF) stockline is important for charging control and BF safety. However, traditional non-contact methods for stockline detection face many challenges such as low accuracy, poor anti-interference ability, and stability in harsh BF environments. The high-temperature novel industrial endoscope (NIE), a visible light detection equipment for BF burden surface (BBS), has captured real-time and continuous video streams of BBS, which provides a brand-new means and ideas for non-contact stockline detection. Based on this, a novel non-contact stockline detection method based on real-time burden surface video streams is proposed. First, an edge detection network based on bootstrap sampling and eigenvector coding clustering (ECC) is established to extract accurate edge distribution of burden surface video streams. Then, an actual imaging region model of the NIE is developed by the latitude circle method to quantify the relationship between the actual imaging region and the BF stockline. Finally, the edge of burden surface video streams is correlated with the corresponding actual imaging region through the NIE imaging model, and the non-contact stockline detection model is derived to realize the BF stockline detection based on video streams. Experiments and industrial applications show that the proposed method is superior to the traditional non-contact stockline detection methods in terms of detection accuracy, stability, and real-time performance.