Infrared Thermal Imaging Smoke Detection Based on Motion and Fuzzy Features

The production process of coking enterprises generates abundant smoke. Their discharge and leakage can pollute the natural environment, endangering the safety of life and production. Considering the low contrast and poor texture of thermal imaging videos, this study detected smoke with motion and fuzzy characteristics. The noise degree of each frame image can be calculated to replace the fixed threshold of the Vibe detection algorithm so that the moving target area can be completely detected. First, the image was divided into block area images; then, the fuzzy-to-noise ratio in this area was extracted by combining the motion area, the features calculated when the fast fourier transform (FFT) was used to calculate the ambiguity were trained to generate a smoke classifier, and finally, the experimental video detection, with an average accuracy rate of 94.53%. The results show that the proposed algorithm is accurate, operates in real-time for smoke detection in infrared thermal imaging videos of coking enterprises, and has good anti-interference ability.