Multi-feature deep learning-based vehicle detection method and apparatus

Abstract

The invention relates to a multi-feature deep learning-based vehicle detection method and apparatus. The method comprises the steps of collecting a standard captured vehicle image by an intelligent network camera; extracting feature information of three channels for the vehicle image, wherein the feature information mainly includes grayscale information, Sobel edge detection in an X direction and Sobel edge detection in a Y direction; inputting the feature information of the three channels to a combination feature convolutional neural network model obtained by pre-training to perform vehicle detection; and outputting a detection result. According to the method and the apparatus, for appearance information and shape information of a vehicle, the three feature channels are extracted, original RGB color features are abandoned, priori knowledge of people to the features is added, and a combination feature convolutional neural network method is used, so that the calculation amount required for training can be reduced, the detection time can be shortened, and the vehicle locating accuracy is improved.

Description

technical field [0001] The present invention relates to the field of intelligent traffic management, and more specifically, relates to a vehicle detection method and device based on multi-feature deep learning. Background technique [0002] Intelligent transportation devices (ITS) use image processing, artificial intelligence, embedded technology, sensors, and pattern recognition to solve problems such as traffic congestion and traffic accidents, thereby improving the safety and effectiveness of road traffic devices. Among them, vehicle detection is an important part of intelligent transportation devices, and has broad application prospects in fields such as traffic guidance and road monitoring of assisted driving devices. [0003] At present, the commonly used vehicle detection methods are: methods based on modeling and template matching and methods based on statistical learning. The method based on modeling and template matching uses local features to describe the vehicle...

AI Technical Summary

Problems solved by technology

At present, the method of using neural network to independently learn and recognize object features has been successfully realized, such as using Convolutional Neural Network (CNN) to independently select vehicle features for learning and recognition. The disadvantage of taking a long time

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