Vehicle target detection method, system and device based on YOLOv2

Abstract

The invention provides a vehicle target detection method based on YOLOv2. The method comprises the steps of collecting pattern data and processing the pattern data to obtain a sample data set; constructing an improved YOLOv2 model, and improving the network structure of the YOLOv2 basic model; designing a loss function by adopting a comparison normalization thought; performing model training, wherein the training process of the whole network is divided into classification network training and detection network training; and model verification: verifying the model obtained by training by usingthe verification data set, and testing the recognition capability of the model. And the algorithm is subjected to experimental comparison with a recently popular algorithm to verify the effectivenessof the algorithm. The improved YOLOv2 model established by the invention can improve the recognition rate of small-scale vehicle targets. Under various complex conditions, most of target vehicles canbe detected, and the recognition speed and precision are higher than those of other conventional algorithms.

Description

technical field [0001] The invention relates to a vehicle target detection method, system and equipment, in particular to a method for identifying and detecting vehicles in complex traffic scenes. Background technique [0002] In recent years, with the growth of market demand and the continuous development of artificial intelligence, autonomous driving has gradually become one of the hot issues studied by various scholars. As an important step in automatic driving, vehicle target detection is also one of the main research issues. In intelligent traffic monitoring video, target detection technology has also been widely used, such as the detection and positioning of vehicles and pedestrians, and the automatic toll collection system of toll stations. At present, in the unmanned driving that has been applied, the target detection technology is also fully utilized. The system uses video and images as the research object, and uses target detection technology to detect and judge w...

AI Technical Summary

Problems solved by technology

However, the detection effect of the YOLOv2 model on small targets is relatively poor, mainly because the image becomes smaller after the operation of the convolution layer and the pooling layer, and the features of the small image are more difficult to extract, and these features are very important for target detection. The accuracy impact is also relatively large, and the target detection accuracy is low

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