Object detecting method and device, image processing equipment and storage medium
A technology for object detection and images to be detected, applied in the computer field, can solve the problems of low object detection and detection speed, insufficient flexibility, insufficient detection accuracy, etc., and achieve the effect of improving speed, improving detection effect, and reducing interference.
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Embodiment 1
[0025] figure 1 The implementation process of the object detection method provided by the first embodiment of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:
[0026] In step S101, the image to be detected is received, and feature extraction is performed on the image to be detected through a pre-trained convolutional neural network to obtain feature maps of the image to be detected in different convolutional layers.
[0027] The embodiments of the present invention are applicable to a platform or system for detecting target objects on an image. When training a convolutional neural network, a training image set can be collected. There are one or more standard frames on each training image in the training image set. These standard frames are used to mark the position and category of the target object on the training image. Therefore, the volume The tr...
Embodiment 2
[0054] figure 2 The structure of the object detection device provided by Embodiment 2 of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, including:
[0055] The feature extraction unit 21 is configured to receive the image to be detected, and perform feature extraction on the image to be detected through a pre-trained convolutional neural network, so as to obtain feature maps of the image to be detected in different convolutional layers.
[0056] In the embodiment of the present invention, feature extraction is performed on the image to be detected through the trained convolutional neural network to obtain feature maps of the image to be detected in different convolutional layers of the convolutional neural network, and the size of the feature maps of different convolutional layers is different. When pre-training the convolutional neural network, the convolutional neural network c...
Embodiment 3
[0087] Figure 4 The structure of the image processing device provided by the third embodiment of the present invention is shown, and for the convenience of description, only the parts related to the embodiment of the present invention are shown.
[0088] The image processing device 4 of the embodiment of the present invention includes a processor 40 , a memory 41 and a computer program 42 stored in the memory 41 and operable on the processor 40 . When the processor 40 executes the computer program 42, the steps in the above-mentioned method embodiments are realized, for example figure 1 Steps S101 to S104 are shown. Alternatively, when the processor 40 executes the computer program 42, the functions of the units in the above-mentioned device embodiments are realized, for example figure 2 Function of units 21 to 24 shown.
[0089] In the embodiment of the present invention, the feature map to be predicted is selected from the feature map of the image to be detected in diff...
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