A low-light image scene understanding method

A scene understanding, low-light technology, applied in the field of improving scene understanding under low light, can solve problems such as difficulty in achieving good results, and achieve the effect of restoring picture details and improving brightness

Inactive Publication Date: 2019-03-01
DALIAN UNIV OF TECH
View PDF2 Cites 27 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] Aiming at the problem that the existing scene understanding method can only operate in an ideal environment, and it is difficult to achieve good results in a non-ideal environment such as low light, the present invention designs a series model of scene understanding based on deep learning technology combined with an image enhancement method The framework, for the input low-light pictures, can first enhance the picture, improve the brightness and recognition of the picture without changing the pixel color information in the picture, and then perform the task of scene understanding

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
  • A low-light image scene understanding method
  • A low-light image scene understanding method
  • A low-light image scene understanding method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] The invention will be described in further detail below in conjunction with specific embodiments, but the present invention is not limited to specific embodiments.

[0035] A method for scene understanding of low-light images based on deep learning, including the training of the network model and the operation steps of the model.

[0036] 1. Training network model

[0037] To train an enhanced network that improves image recognition, a sufficient data set must first be prepared. As shown in Figure 1, each data set contains three images, normal images, low-light images generated by adjusting parameters such as the Gamma value with PS tools, and corresponding semantic segmentation labels. The entire dataset contains 2975 sets of high-resolution street view data as a training set, and 456 sets of data as a validation set. In order to improve the generalization ability of the model, the training data is divided into 10 groups, and each group of data is modified and synthe...

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 belongs to the technical field of computer vision, and provides a low-light image scene understanding method. A frame of series model of scene understanding based on depth learning and image enhancement is designed. For the input low-light image, the image is enhanced first to improve the brightness and discrimination without changing the color information of pixels in the image. Inthis framework, the task of scene understanding in low-lighting images is solved. The visual enhancement network provided by the invention can improve brightness on the basis of keeping picture colorinformation through jump connection operation, combining low-level color and structure information and high-level semantic information while restoring details, thereby restoring picture details. The restored image will effectively facilitate the detection, segmentation and other scene understanding tasks. Comparing qualitatively and quantitatively, the method proposed by the invention is superiorto the existing picture enhancement methods in both synthetic and real pictures.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for improving scene understanding under low light using an image enhancement technology based on deep learning. Background technique [0002] Scene understanding (Scenes Understanding), such as object detection and semantic segmentation, is playing an increasingly important role in the field of robot navigation and autonomous driving, because it can provide key visual information for behavioral decisions of robots or vehicles. However, in previous work, existing scene understanding methods mainly focus on clearly recognizable images or video frames captured in sunny weather, but for images captured in bad weather conditions or at night, due to insufficient illumination The loss of details, unclear edges, and pixel distortion of the captured image will lead to a serious reduction in the quality of image scene understanding. This kind of scene is often...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T5/40G06N3/04
CPCG06T5/001G06T5/40G06N3/045
Inventor 王昊然杨鑫魏小鹏尹宝才张强
Owner DALIAN UNIV OF TECH
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