Detection of motor vehicle carbon emissions on urban road sections based on video sensing technology

With the rapid urbanization and the exponential growth of the urban population in China, urban road transportation has become the primary mode of travel for residents. However, motor vehicles have led to an alarming increase in greenhouse gas emissions, posing a serious environmental challenge for China. Traditional carbon emission calculation methods are generally used to estimate the total carbon emissions over a macro-region within a specific time period, but they have limitations when calculating the carbon emissions per unit of time for road sections where vehicles pass through. To address this issue, we focus on a typical urban road section and combine it with the carbon emission calculation formula. Under the framework of deep learning, we employ a Convolutional Neural Network (CNN) to study the motor vehicle driving parameter detection method, and conduct research on the carbon emission of motor vehicles on urban roads. The system has a low investment cost and enables the analysis of regular carbon emission patterns at specific locations, providing a solid theoretical basis for establishing an early warning mechanism for excess carbon emissions. In the future, it can evolve into an information exchange platform for transportation and environmental protection departments, offering promising applications and practical value.