Target detection method and device based on multi-expert model, storage medium and equipment
A target detection and model technology, applied in the field of image processing, can solve the problems that affect the target detection and recognition rate, and the neural network cannot accurately process multiple target candidate areas, so as to achieve the effect of improving the recognition rate
Pending Publication Date: 2021-06-11
无锡禹空间智能科技有限公司
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[0005] The embodiment of the present application provides a target detection method, device, storage medium and equipment based on a multi-expert model, which is used to solve the problem that a single neural network cannot accurately process multiple target candidate regions, thereby affecting the recognition rate of target detection
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[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the following will further describe the embodiments of the present application in detail in conjunction with the accompanying drawings.
[0050] Please refer to figure 1 , which shows a method flow chart of a multi-expert model-based target detection method provided by an embodiment of the present application. The multi-expert model-based target detection method can be applied to a neural network model, which includes a fast area Convolutional Neural Networks and Multi-Expert Models. The target detection method based on the multi-expert model may include:
[0051] Step 101, acquire an image to be recognized, and the image includes at least one target object.
[0052] Wherein, the image to be recognized may be taken by an electronic device, or may be obtained from another electronic device, and this embodiment does not limit the source of the image. ...
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The invention discloses a target detection method and device based on a multi-expert model, a storage medium and equipment, and belongs to the technical field of image processing. The method is used in a neural network model, the neural network model comprises a fast regional convolutional neural network and a multi-expert model, and the method comprises the following steps: a to-be-recognized image being acquired, wherein the image comprises at least one target object; processing the image through a fast regional convolutional neural network to obtain a plurality of candidate feature maps; determining a candidate feature map matched with each expert model in the multi-expert model; and processing the matched candidate feature maps through each expert model to obtain the category and position of each target object. According to the method and the device, different candidate feature maps can be processed by utilizing different expert models, so that the different candidate feature maps can be processed by the expert model which is good at processing the data region, and the recognition rate of target detection is improved.
Description
technical field [0001] The embodiments of the present application relate to the technical field of image processing, and in particular to a multi-expert model-based object detection method, device, storage medium and equipment. Background technique [0002] Object detection refers to the detection of objects of interest from images, including object localization and classification. In recent years, object detection algorithms have made great breakthroughs. [0003] One of the more popular algorithms now includes two stages (two-stage), that is, the neural network model can first propose multiple target candidate areas from the image, and then classify and return multiple target candidate areas to obtain the image The categories and locations of different target objects in . [0004] As the size of the data set increases, a single neural network model is often only good at processing a part of the data, that is, a single neural network cannot accurately process multiple tar...
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IPC IPC(8): G06K9/62G06K9/42G06K9/34G06N3/04
CPCG06V10/32G06V10/267G06N3/045G06F18/22G06F18/24
Inventor 王堃
Owner 无锡禹空间智能科技有限公司