Diesel black smoke vehicle detection method

Abstract

The invention discloses a diesel black smoke vehicle detection method, which belongs to the technical field of environment detection, and comprises the following steps of: constructing a black smoke vehicle sample data set by utilizing monitoring pictures of a road, and marking a rectangular boundary frame where a black smoke vehicle is located in the monitoring pictures; calculating a priori boxcontaining the diesel black smoke vehicle by using a k-mean clustering algorithm for the rectangular boundary box marked on the monitoring picture; training the constructed target detection model by using the monitoring picture and a priori frame containing the diesel black smoke vehicle to obtain a trained model; and detecting the to-be-detected monitoring picture by using the trained model, andjudging whether a black smoke vehicle exists in the to-be-detected monitoring picture or not. According to the method, the priori frame is obtained by clustering in the new data set, the positioning of the target in the monitoring picture is facilitated, the recall rate of the network is improved by predicting the deviation value of the rectangular boundary frame relative to the priori frame, andthe detection capability of the network is enhanced.

Description

technical field [0001] The invention relates to the technical field of environmental detection, in particular to a detection method for a diesel black smoke vehicle. Background technique [0002] The black smoke exhaust gas emitted by diesel vehicles contains about 200 different compounds, which is one of the main causes of fine particulate matter and photochemical smog pollution. Considering the mileage and emission coefficient of diesel vehicles comprehensively, the nitrogen oxide and fine particle 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 blurred vision, 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 features of video smoke and ...

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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