Method for detecting safety belt based on deep CNN

Abstract

The invention belongs to the field of intelligent transportation and relates to a method for detecting a safety belt based on the adoption of deep CNN of image at traffic monitoring points. The method establishes a sample database and conducts deep CNN training on the sample database, and adopts training parameters to determine whether a driver in a newly input image wears a safety belt. The method comprises the following steps of extracting a gray scale image of a driver area, establishing a sample database, designing a deep CNN network, training the deep CNN network and determining the result of individual image. The method extracts a window area by using upper and lower edge information of a front window of a vehicle, conducts auxiliary positioning of the driver area by using a plate area, does not need to use characteristics of manual design classification, and has high precision in determining whether the driver wears the safety belt. The method has simple principle, is easy to operate, has small errors, is safe and reliable, has strong application values, and is environmental friendly.

Description

Technical field: [0001] The invention belongs to the field of intelligent transportation, and relates to a safety belt detection method based on a traffic checkpoint image using a deep CNN (convolutional neural network). By establishing a sample library and performing deep convolutional neural network training on it, the training parameters are used to judge Whether the driver in the newly input image is wearing a seat belt or not. Background technique: [0002] With the improvement of the traffic management system and electronic monitoring, drivers generally wear seat belts when driving, but there are still a large number of drivers who do not wear seat belts, especially truck drivers, taxi drivers and those who lack supervision and mainly drive in the suburbs. Moreover, the phenomenon of co-pilot passengers not wearing seat belts is even worse, especially when taking a taxi, the co-pilot passengers hardly wear seat belts, because they do not pay enough attention to driving...

AI Technical Summary

Problems solved by technology

The disadvantage of existing methods based on traditional image processing is that in the actual traffic checkpoint image processing, the degree of sample diversity is serious, and the interference of different lighting, different vehicles, and driver's clothes makes the seat belt detection method based on image processing inefficient. low, low precision

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