Lightweight cattle herd detection method and device based on DC-SMKD

A detection method and lightweight technology, applied in the field of computer vision, to achieve the effect of improving the accuracy of cattle detection

Pending Publication Date: 2022-05-13
易采天成(郑州)信息技术有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] One purpose of this application is to provide a DC-SMKD-based lightweight cattle detection method and equipment to solve how to improve the lightweight cattle detec...

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  • Lightweight cattle herd detection method and device based on DC-SMKD
  • Lightweight cattle herd detection method and device based on DC-SMKD
  • Lightweight cattle herd detection method and device based on DC-SMKD

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Embodiment Construction

[0046] The application will be described in further detail below in conjunction with the accompanying drawings.

[0047] In a typical configuration of the present application, the terminal, the device serving the network, and the trusted party all include one or more processors (such as a central processing unit (Central Processing Unit, CPU), an input / output interface, a network interface, and a memory.

[0048] Memory may include non-permanent memory in computer-readable media, random access memory (Random Access Memory, RAM) and / or non-volatile memory, such as read-only memory (Read Only Memory, ROM) or flash memory (flash RAM). Memory is an example of computer readable media.

[0049] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can be implemented by any method or technology for storage of information. Information may be computer readable instructions, data structures, modules of a program, or other data. Exampl...

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Abstract

The invention provides a DC-SMKD-based lightweight cattle herd detection method and equipment, and the method comprises the steps: carrying out the depth separable convolution of a classical model, reducing the size of the model, obtaining a student model, carrying out the knowledge distillation of each layer of features outputted by the student model and a teacher model through SmoothL1Loss, enabling the student model to learn the multi-layer feature information of the teacher model YOLOv5x, and enabling the learning efficiency of the student model to be improved. Feature loss after separable convolution is made up, relatively complete cattle body features in a teacher model can be acquired, and cattle herd detection accuracy is improved.

Description

technical field [0001] The present application relates to the field of computer vision, in particular to a method and device for lightweight cattle herd detection based on Depthwise Separable Convolution-Smooth Multi-layer Perceptual Knowledge Distillation (DC-SMKD). Background technique [0002] In the existing technology, in recent years, the YOLO series of algorithms have brought a new idea to the field of cattle detection, that is, the fusion of classification tasks and positioning tasks. After the image features are extracted through the backbone network, further classification regression is directly performed on this feature. and position positioning, and finally obtain the position information and category information of the object. Especially the latest YOLOv5 model, which uses an end-to-end design idea to extract full-image features. It not only further reduces the size of the network model, but also ensures the speed and accuracy of detection. However, the size o...

Claims

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Application Information

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IPC IPC(8): G06V20/20G06N3/04G06N3/08
CPCG06N3/08G06N3/047G06N3/045
Inventor 沈雷徐溢凡
Owner 易采天成(郑州)信息技术有限公司
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