Object detection method, device and electronic apparatus
An object detection and object technology, which is applied in character and pattern recognition, instruments, computer components, etc., can solve the problem of difficult coverage of each object, and achieve the effects of small granularity, improved accuracy, and easy modeling
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Embodiment 1
[0037] refer to figure 1 , shows a flow chart of steps of an object detection method according to Embodiment 1 of the present invention.
[0038] The object detection method of this embodiment includes the following steps:
[0039] Step S102: Obtain multiple superpixels in the image to be detected.
[0040] Wherein, the image to be detected is an image after superpixel segmentation. In this embodiment, a plurality of superpixels in the image are obtained from the image after superpixel segmentation. In the embodiments of the present invention, multiple means two or more.
[0041] In practical applications, any appropriate superpixel segmentation method can be used to obtain superpixels in a picture, including but not limited to superpixel segmentation methods based on graph theory, such as graph-based method, Ncut method, superpixel lattice method , methods based on entropy rate, etc.; or methods based on gradient descent, such as watershed method, MeanShift method, Quick-...
Embodiment 2
[0050] refer to figure 2 , shows a flow chart of steps of an object detection method according to Embodiment 2 of the present invention.
[0051] In this embodiment, an energy function for object detection is first trained, and then the energy function is used for image detection to determine the category and / or position of the object in the image. The object detection method of this embodiment includes the following steps:
[0052] Step S202: Obtain sample images for training.
[0053] Wherein, the sample image includes the information of the segmented superpixels.
[0054] Step S204: using the sample image to train an energy function.
[0055] In a feasible manner, the sample image can be used to train an energy function based on RCNN (Region based Convolutional Neural Network) and a set objective function until the energy function has a significant impact on the sample image. The annotations of the superpixels meet the set training termination conditions. The objectiv...
Embodiment 3
[0070] refer to image 3 , shows a flow chart of steps of an object detection method according to Embodiment 3 of the present invention.
[0071] In this embodiment, the energy function trained for object detection is further described. After the energy function is trained, the energy function is used for image detection to determine the category and / or location of the object in the image.
[0072] The object detection method of this embodiment includes the following steps:
[0073] Step S302: Obtain a sample image, perform superpixel segmentation on the sample image, and use the sample image after superpixel segmentation as a sample image for training.
[0074] In this embodiment, the superpixel segmentation result of each sample image used for training is denoted as P, where P={p 1 ,p 2 ,...,p N}, p i is the i-th superpixel, and N is the number of superpixels; if p i and p j connected together in space, then p i and p j belongs to a neighborhood system X, that is, ...
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