Multi-scale joint network based on Retinex theory for low-light enhancement

Song, Xijuan; Huang, Jijiang; Cao, Jianzhong; Song, Dawei

doi:10.1007/s11760-021-01856-y

Cited by 11 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IFM-based image enhancement utilizes the physical properties of water to restore underwater images. For instance, the underwater image enhancement algorithm based on the Retinex theory (Song et al , 2021) combines contrast and color correction. Dark channel prior (DCP) algorithm (He et al , 2011) solved the restoration of images in foggy air through DCP theory.…”

Section: Improved Relative Global Histogram Stretching Underwater Ima...mentioning

confidence: 99%

Research on small sample target detection for underwater robot

Luo,

Ruan,

2024

RIA

View full text Add to dashboard Cite

Purpose The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments. Design/methodology/approach The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information. Findings The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets. Originality/value Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.

show abstract

Section: Improved Relative Global Histogram Stretching Underwater Ima...mentioning

confidence: 99%

Research on small sample target detection for underwater robot

Luo,

Ruan,

2024

RIA

View full text Add to dashboard Cite

show abstract

“…The MSRCR algorithm, multiscale retinal enhancement with color recovery, was developed based on the singlescale Retinex algorithm and the multiscale weighted average MSR algorithm. Retinex is an image enhancement algorithm as a word consisting of the words retina (retina) and cortex (cortex) [25]. Retinex theory is based on the idea that the…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Target Recognition Algorithm Based on Optical Sensor Data Fusion

Cao

2021

Journal of Sensors

View full text Add to dashboard Cite

Optical sensor data fusion technology is a research hotspot in the field of information science in recent years, which is widely used in military and civilian fields because of its advantages of high accuracy and low cost, and target recognition is one of the important research directions. Based on the characteristics of small target optical imaging, this paper fully utilizes the frontier theoretical methods in the field of image processing and proposes a small target recognition algorithm process framework based on visible and infrared image data fusion and improves the accuracy as well as stability of target recognition by improving the multisensor information fusion algorithm in the photoelectric meridian tracking system. A practical guide is provided for the solution of the small target recognition problem. To facilitate and quickly verify the multisensor fusion algorithm, a simulation platform for the intelligent vehicle and the experimental environment is built based on Gazebo software, which can realize the sensor data acquisition and the control decision function of the intelligent vehicle. The kinematic model of the intelligent vehicle is firstly described according to the design requirements, and the camera coordinate system, LiDAR coordinate system, and vehicle body coordinate system of the sensors are established. Then, the imaging models of the depth camera and LiDAR, the data acquisition principles of GPS and IMU, and the time synchronization relationship of each sensor are analyzed, and the error calibration and data acquisition experiments of each sensor are completed.

show abstract

“…To solve the problem caused by manual visual inspection and meet the needs of production, a more advanced method is introduced to detect surface defects. Machine vision is adopted to replace human eyes with "machine" for bearing surface defect detection [2].…”

Section: Introductionmentioning

confidence: 99%

Machine vision-based surface defect detection method for rolling bearings of metro vehicle

Ge¹

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

View full text Add to dashboard Cite

As the core component of the subway running gear, the safety and reliability of the bearing has been widely concerned. Under the influence of high speed, heavy load, long routing, sand erosion, rain corrosion and other operating conditions, the service conditions of bearings become very harsh, and damage inevitably occurs. At present, most of the nondestructive testing technologies used in metro bearings are black magnetic particle testing technology, which has the advantages of high sensitivity, high reliability and easy identification of defects. It is one of the most effective technologies widely recognized for the effective inspection of the surface or near-surface of ferromagnetic materials. To detect the surface defects of rolling bearings of metro vehicles, the images of black magnetic particle flaw detection defects of metro vehicles’ rolling bearings are collected on the spot. By using image processing technology, all binary images of surface defects are extracted, and a data set including the original image and binary images of surface defects is established. It is pointed out that there are great difficulties and limitations in using traditional machine vision technology to identify subway bearing flaw detection defects. Thus, convolutional neural network is chosen to identify bearing defects to realize deep feature fusion. Through the comparison with several models, it is concluded that there is a certain improvement in the effect and accuracy of surface defect identification in this paper, which can not only reduce the labor intensity and training costs of personnel, but also ensure the efficiency and accuracy of bearing defect detection identification. It is the trend and hot spot of future development.

show abstract

Multi-scale joint network based on Retinex theory for low-light enhancement

Cited by 11 publications

References 28 publications

Research on small sample target detection for underwater robot

Research on small sample target detection for underwater robot

Target Recognition Algorithm Based on Optical Sensor Data Fusion

Machine vision-based surface defect detection method for rolling bearings of metro vehicle

Contact Info

Product

Resources

About