Image quality measurement-based comparative analysis of illumination compensation methods for face image normalization

Hassan, Mannan; Shaikh, Muhammad Suhail; Jatoi, Munsif Ali

doi:10.1007/s00530-021-00853-y

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…In order to reduce the influence of light scattering and reflection, the RGB data set is normalized and then converted into a grayscale map by the weighted average method, so that the model can read the grayscale values uniformly [10,11]. R, G, and B are the normalized RGB values, respectively, and Gray represents the gray value.…”

Section: Steel Crack Identification Methodsmentioning

confidence: 99%

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

et al. 2023

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A method of steel structure surface crack identification based on artificial intelligence technology is proposed to solve the problem that steel cracks can not be detected and forewarned in time when they appear in the railway industrial environment. The appearance of steel cracks greatly weakens the stability of steel structures, and will seriously endanger the safety of the railway industry if it is not detected and repaired in time. However, the common steel crack detection methods cannot achieve real-time monitoring of steel structures. In order to monitor the surface of steel structure in real-time and explore the recognition effect and model the advantages of common classification neural network models for surface cracks of railway industrial steel, this study evaluates the network model with multiple indicators and parameters under two experimental conditions. In this study, the steel surface cracks in the railway industrial environment are taken as samples, and the steel cracks are identified through the neural network model. For large-volume datasets, the recognition accuracy of the three network models has reached 97%, of which the YOLOv5 model has the best comprehensive recognition ability, and the C-Alex model has the best performance and convergence speed in small-volume datasets. This study explores the application prospects of models under different scenarios, proving that the three models can effectively detect steel surface cracks in real-time, and at the same time, it will pave the way for the development and application of artificial intelligence multi-model fusion technology in the field of the railway industry.

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Section: Steel Crack Identification Methodsmentioning

confidence: 99%

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

et al. 2023

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“…Various techniques and their diverse configurations are employed, as referenced in prior research [22,23]. Among these, thresholding with binarization [24,25] and histogram analysis [26,27] represent the most classical and enduring methods, known for their simplicity and longevity in practical application.…”

Section: Introductionmentioning

confidence: 99%

The Verification of the Correct Visibility of Horizontal Road Signs Using Deep Learning and Computer Vision

Kulawik,

Kubanek,

Garus

2023

Applied Sciences

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This research aimed to develop a system for classifying horizontal road signs as correct or with poor visibility. In Poland, road markings are applied by using a specialized white, reflective paint and require periodic repainting. Our developed system is designed to assist in the decision-making process regarding the need for repainting. It operates by analyzing images captured by a standard car camera or driving recorder. The image data undergo initial segmentation and classification processes, facilitated by the utilization of the YOLOv4-Tiny neural network model. The input data to the network consist of frames extracted from the video stream. To train the model, we established our proprietary database, which comprises 6250 annotated images and video frames captured during driving. The annotations provide detailed information about object types, their locations within the image, and their sizes. The trained neural network model effectively identifies and classifies objects within our dataset. Subsequently, based on the classification results, the identified image fragments are subjected to further analysis. The analysis relies on assessing pixel-level contrasts within the images. Notably, the road surface is intentionally designed to be dark, while road signs exhibit relatively lighter colors. In conclusion, the developed system serves the purpose of determining the correctness or visibility quality of horizontal road signs. It achieves this by leveraging computer vision techniques, deep learning with YOLOv4-Tiny, and a meticulously curated database. Ultimately, the system provides valuable information regarding the condition of specific horizontal road signs, aiding in the decision-making process regarding potential repainting needs.

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Adapted type-II fuzzy algorithm to process images with non-uniform illumination

Al-Ameen

2024

SIViP

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Image quality measurement-based comparative analysis of illumination compensation methods for face image normalization

Cited by 4 publications

References 34 publications

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

The Verification of the Correct Visibility of Horizontal Road Signs Using Deep Learning and Computer Vision

Adapted type-II fuzzy algorithm to process images with non-uniform illumination

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