Abstract
Object detection is a very important task in artificial intelligence and deep learning. Its purpose is to automatically locate the position of an object in a picture and mark the type of the object. In order to solve the problem that the number of false checks in the process of traditional detection methods is too large to affect the accuracy of detection results, a multi-objective real-time detection method for vehicles based on yolov5 is designed. The design of vehicle multi-target real-time detection method is completed by building vehicle multi-target detection model based on yolov5, acquiring vehicle video image information and real-time vehicle multi-target tracking detection. Through comparative experiments, it is further proved that the designed detection method can effectively reduce the number of false checks, improve the accuracy of monitoring results, and meet the accuracy requirements of the Intelligent Transportation Command Center for vehicle information acquisition.
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