据红外技术网，于2024年04月报道，基于运动与模糊特征的红外热成像烟雾检测。

        摘要: 在焦化企业生产过程中会产生大量烟雾，排放、泄漏会对自然环境造成污染危及生命生产安全。针对热成像视频对比度低、纹理度差等特点，本文采用烟雾具有运动与模糊特性来进行检测。提出计算每帧图像的噪点度来改进Vibe检测算法的固定阈值，从而可更完整地将移动目标区域检测出来。将整幅图像划分为块区域图像；结合运动区域来提取该区域内的模糊与噪点比值、FFT计算模糊度所计算特征训练生成烟雾分类器；对实验视频进行检测，平均准确率达到94.53%。结果表明，本文所提算法对焦化企业红外热成像视频烟雾检测的准确性和实时性，具有良好的抗干扰能力。

        关键词: 改进Vibe模糊烟雾特征 /  红外热成像 /  烟雾检测 

        Abstract: 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.

Key words: improved Vibe fuzzy smoke features /  infrared thermal imaging /  smoke detection