A kind of detection method of diesel black smoke vehicle

Abstract

The invention discloses a detection method for diesel smoky vehicles, belonging to the technical field of environmental detection. ; Use the k-means clustering algorithm on the rectangular bounding box marked on the monitoring picture to calculate the a priori box containing the diesel smoky car; use the monitoring picture and the a priori box containing the diesel smoky car to construct the target detection model Carry out training to obtain a trained model; use the trained model to detect the monitoring pictures to be detected, and determine whether there is a black smoke vehicle in the monitoring pictures to be detected. The invention obtains a priori frame by clustering in a new data set, which is more conducive to the positioning of the target in the monitoring picture. Test your ability.

Description

technical field [0001] The invention relates to the technical field of environmental detection, in particular to a detection method of a diesel black smoke vehicle. Background technique [0002] The black smoke exhaust emitted by diesel vehicles contains about 200 different compounds, which are one of the main causes of fine particulate matter and photochemical smog pollution. Taking into account the mileage and emission factor of diesel vehicles, the nitrogen oxides and fine particulate matter emissions of a diesel vehicle that cannot meet the National III emission standards or lower emission standards are equivalent to the emissions of more than 200 National IV emission standard cars. Sum. [0003] Based on the characteristics of visual blur, translucency, particle characteristics and diffusion motion of smoke, many experts and scholars have put forward a lot of related work on smoke detection in recent years. According to the color characteristics of video smoke and the...

AI Technical Summary

Problems solved by technology

The artificial feature extraction methods based on empirical thresholds and experimental statistical thresholds used in traditional smoke detection algorithms, such as artificially designing and modeling smoke color features, frequency domain features, texture features, etc., are difficult to perform detection tasks in such complex scenes

[0008] (2) All computing tasks of the machine learning algorithm designed by the traditional method can only be deployed on the CPU, the real-time performance is poor, and it is difficult to perform specific optimization according to specific application scenarios and support GPU parallel computing acceleration

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