Target detection method and device

Abstract

The embodiments of the present invention provide a target detection method and device. The method includes the following steps that: an image to be detected is acquired; a plurality of candidate areasof the image to be detected are classified according to a cascade neural network, at least one level of neural network of neural networks starting from the second-level neural network includes a plurality of parallel sub neural networks of the corresponding level, wherein the sub-neural networks classify classification results of a previous level of neural network; and a target area is determinedaccording to the final classification results of the plurality of candidate areas. According to the method and device provided by the embodiments of the present invention, at least one level of neural network of the neural networks starting from the second-level neural network includes the plurality of parallel sub neural networks at the corresponding level, so that the candidate areas can be classified more comprehensively and accurately, and therefore, classification accuracy can be improved, and the target area can be accurately determined; and the reduction of the neural networks can be benefitted, and storage space occupied by a classification model composed of various levels of neural networks can be decreased. The method and device can be applied to devices with low hardware configurations or low computing performance.

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular, the present invention relates to a target detection method and device. Background technique [0002] Object detection is a traditional research direction in the field of computer vision. [0003] Traditional object detection methods, such as Adaboost (adaptive boosting classifier boosting) algorithm combined with Haar (Haar wavelet) features or LBP (Local Binary Pattern, local binary pattern) and other features have been widely used. However, it is difficult to further improve the performance of such traditional methods in terms of detection rate. [0004] At present, the main challenge of the target detection algorithm is that the target is easily disturbed, which makes it difficult to improve the performance such as detection rate. For example, the face in the target is susceptible to diversity caused by the influence of face posture, skin color, illumination, o...

AI Technical Summary

Problems solved by technology

[0009] In view of the shortcomings of the existing methods, the present invention proposes a target detection method and device to solve the problems of low detection rate or large storage space occupied by classification models in the prior art, so as to improve the detection of targets in images rate or reduce the classification model, so that object detection is suitable for devices with lower hardware configuration or weaker computing performance

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