Target detection method and device

A target detection and target technology, applied in the field of data processing, can solve the problem of low accuracy of small target detection

Pending Publication Date: 2020-10-23
BEIJING WEIBOYI TECH CO LTD
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the main purpose of the present invention is to solve the pr

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Target detection method and device
  • Target detection method and device
  • Target detection method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0024] Such as figure 1 As shown, the present invention provides a method for target detection, comprising:

[0025] Step 101, obtain the file to be detected.

[0026] In this embodiment, the file to be detected in step 101 may be a video to be detected, or a picture to be detected, which is not limited here.

[0027] Step 102: Detect the file to be detected by using the pre-trained target detection model to obtain the target to be detected.

[0028] In this embodiment, the pre-trained target detection model in step 102 is obtained by training the extended YOLOv3 model through pictures containing the target in advance, and the extended YOLOv3 model is a scale-expanded model of the preset initial YOLOv3 model. By expanding the scale of the initial YOLOv3 model, the representation ability of shallow features can be enhanced, thereby improving the detection effect and accuracy of small targets. The initial YOLOv3 model is the YOLOv3 model in the prior art.

[0029] In this em...

Embodiment 2

[0056] Such as Figure 8 As shown, the present invention provides a target detection device, comprising:

[0057] A file acquisition module 801, a file detection module 802 and a pre-trained target detection model 803;

[0058] The file obtaining module is used to obtain the file to be detected;

[0059] The file detection module is connected with the file acquisition module and the pre-trained target detection model respectively, and is used to detect the file to be detected by the pre-trained target detection model to obtain the target to be detected;

[0060] The pre-trained target detection model is obtained by pre-training the extended YOLOv3 model through the pictures containing the target, and the extended YOLOv3 model is the scale-expanded model of the preset initial YOLOv3 model.

[0061] In this embodiment, the process of realizing target detection through the file acquisition module 801 , file detection module 802 and pre-trained target detection model 803 is simi...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a target detection method and device, and relates to the field of data processing. The objective of the invention is to solve the problem of low accuracy of small target detection in the prior art. The technical scheme provided by the embodiment of the invention comprises the steps of obtaining a to-be-detected file; detecting the to-be-detected file through a pre-trained target detection model to obtain a to-be-detected target; wherein the pre-trained target detection model is obtained by training an extended YOLOv3 model through a picture containing the target in advance, and the extended YOLOv3 model is a model obtained by performing scale extension on a preset initial YOLOv3 model. The scheme can be applied to target detection of pictures, short videos and the like.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a target detection method and device. Background technique [0002] Object detection is a popular research topic in the field of computer vision, and it has broad application prospects in many fields such as face recognition, security monitoring, dynamic tracking, and image recognition. Object detection refers to detecting and identifying specific objects in specific scenes / pictures, and outputting information such as the position and size of specific objects. [0003] In the prior art, YOLOv3 is generally used to realize target detection. YOLOv3 is a target detection network in deep learning. It is widely used in the detection and recognition of single-frame images. Compared with traditional target detection algorithms, its advantages lie in higher detection accuracy and faster detection speed. [0004] However, because the YOLOv3 neural network is too deep, it is easy to ignore...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/46G06V2201/07G06N3/045G06F18/214
Inventor 邓积杰何楠林星白兴安徐扬
Owner BEIJING WEIBOYI TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap