Intelligent visual calibration technology for Ringelman smoke blackness chart based on Mask R-CNN and topological analysis

The Ringelman smoke blackness chart is an important basis for evaluating the degree of smoke emission pollution. Traditional calibration methods face challenges such as strong subjectivity and low efficiency. This paper proposes an intelligent visual calibration technique for the Ringelman smoke blackness chart, based on Mask R-CNN and topological analysis. This technology combines the powerful image recognition capabilities of deep learning and the precise feature extraction capabilities of topological analysis. First, the Mask R-CNN model is used to segment and recognize smoke images, applying deep learning-based object detection algorithms to identify and locate the blackness chart in the image, while also outputting the blackness level and bounding boxes. Next, topological analysis techniques are employed to extract features from the segmented smoke regions and construct blackness feature vectors. Finally, a classification algorithm is used to categorize the feature vectors, determining the smoke blackness level. Experimental results show that this method provides high recognition accuracy and stability, offering strong technical support for smoke emission monitoring.