Traditional smoke measurement methods for diesel vehicles mainly include Lingman blackness telescope and smoke meter. The Lingman Blackness Telescope uses a binocular prism telescope system. On the telescope partition board, a gray scale step block with corresponding Lingman smoke concentration of 1-5 level is made, and the transparent part is O level. The observer compares the smoke target with the gray gradient block through the left eyepiece of the telescope to determine the standard grade of smoke blackness. Smoke is divided into sampling part and measuring part. In the sampling part, the sampling cylinder extracts a certain volume of smoke from the exhaust pipe so that it passes through a certain area of white filter paper, so the particulate matter in the exhaust gas adheres to the filter paper and makes the filter paper black. Then, the smoke traces on the filter paper are measured by photoelectric detection device to evaluate the exhaust smoke of diesel engine. The above method can measure the smoke level of diesel vehicle exhaust to a certain extent, but it has great application limitations in practice, or cannot be applied in practice. For example, (I) Although the level of diesel vehicle exhaust smoke can be measured with the Lingman blackness telescope, it cannot record the level of diesel vehicle exhaust smoke and the relevant information of the vehicle; (2) The Lingman blackness telescope must be measured manually. It is time-consuming and laborious, and it is impossible for traffic police to observe every vehicle 24 hours with Lingman blackness telescope; (3) Smokemeter can only measure static diesel vehicles, but cannot measure driving vehicles. In view of the above problems, this paper introduces digital image processing technology, through the analysis of digital image of diesel vehicle exhaust, realizes the dynamic detection of diesel vehicle exhaust.

Fawan Almulihi. Image Processing Technology in Emission of Intelligent Traffic Diesel Vehicle. International Journal of Multimedia Computing  (2022), Vol. 3, Issue 3: 47-62. https://doi.org/10.38007/IJMC.2022.030304.

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