Vehicle type identification method based on machine vision and deep learning

Abstract

The invention discloses a vehicle type identification method based on machine vision and deep learning. At present, most of the vehicle identification fields take images collected by high-altitude cameras as data sets, and images collected by mobile platforms are rarely taken as data sets. If a traditional image recognition technology is used, the requirement for mobile violation evidence obtaining cannot be met. According to the method, firstly, image information of a road automobile is collected through a vehicle-mounted mobile platform, preliminary automobile target detection and recognition are conducted through a yolov3 algorithm in deep learning, and then whether the image information is sent to three classifiers for re-prediction or not is comprehensively judged according to a detection frame and a prediction value threshold value. According to the detection results of the three classifiers and a target detection algorithm result, whether the detection box is subjected to errordetection and deletion or not is determined. Finally, a detection result of the system is updated. The method is suitable for the field of vehicle identification of the vehicle-mounted mobile platformin a non-limited operation environment, and achieves a better effect in an actual application scene.

Description

technical field [0001] The invention belongs to the field of machine vision (or intelligent transportation), and in particular relates to a vehicle type recognition algorithm based on machine vision and deep learning. Background technique [0002] At present, machine vision and deep learning technology are widely used in intelligent transportation systems, such as license plate recognition, traffic flow detection, vehicle violation detection, road vehicle type recognition and other fields. [0003] Among them, vehicle recognition refers to the application of machine vision technology to use the digital image or video collected by the camera as image input, and use the target detection framework in deep learning to identify the type of vehicle in the image, and use it as one of the basis for judging vehicle violations. [0004] However, in the field of intelligent transportation, because some images are collected through the use of vehicle-mounted mobile platforms, the overla...

AI Technical Summary

Problems solved by technology

[0004] However, in the field of intelligent transportation, because some images are collected through the use of vehicle-mounted mobile platforms, the overlapping and occlusion of vehicles in the images are serious

In addition, the same type of vehicles in the image is more obvious, which directly leads to a higher error rate in the use of traditional target detection methods.

Images