A method and device for target detection

Abstract

An embodiment of the present invention provides a target detection method and device, wherein the target detection method includes: determining the size of the first convolution kernel according to the preset size of the target to be detected; using the first convolution kernel to convolve the original image Product operation to obtain the initial feature map of the original image; determine the feature extraction model that contains multiple preset feature extraction layers, wherein the feature extraction model is used to convolve through multiple preset feature extraction layers based on the initial feature map According to the feature information, the category of the target to be detected and the position information of the target to be detected in the original image are obtained through the preset multi-category regression algorithm and the preset position regression algorithm. Through the present invention, the actual operating efficiency of target detection can be improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a target detection method and device. Background technique [0002] Object detection is an important technology involving computer vision and image processing. Its main purpose is to detect certain types of object instances in images and videos, such as humans, buildings, vehicles, etc. Object detection technology is currently used in image retrieval, video surveillance, and human-computer interaction. Traditional object detection methods are mostly carried out by artificially constructing features combined with machine learning. This type of method is suitable for situations where the scene is fixed, the target pose does not change much, and the lighting conditions do not change much. [0003] In view of the problems existing in the above-mentioned traditional target detection methods, the method of extracting features by deep learning came into being. The mai...

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Problems solved by technology

[0004] However, the method based on the convolutional neural network has an extremely large amount of calculation when calculating the feature image of each layer, far exceeding DSP (Digital Signal Processor, digital signal processor) and ARM (Advanced Reduced Instruction Set Computer Machines, simplified instruction Integrating the processing power of the computer microprocessor) chip, resulting in the actual operation efficiency of target detection is very low, and when reducing the amount of calculation, it is easy to affect the effect of feature extraction

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