Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review

Azouz, Zakrya; Honarvar Shakibaei Asli, Barmak; Khan, Muhammad

doi:10.3390/electronics12183862

Cited by 5 publications

(3 citation statements)

References 129 publications

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“…Furthermore, foundational knowledge of digital image processing has underpinned many traditional segmentation methods [44]. However, it is crucial to acknowledge that these traditional techniques often face difficulties when handling noisy or non-uniformly illuminated images [45,46].…”

Section: Traditional Segmentation Methodsmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation and pave the way for further research and method integration to enhance this critical healthcare application.

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Section: Traditional Segmentation Methodsmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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“…In the preprocessing phase, a rigorous set of methodologies were employed to refine and optimize the dataset for subsequent analyses [4], [18]. Initially, the dataset underwent a pivotal resizing procedure, transitioning images to a standardized dimension to ensure consistency across the board.…”

Section: Datasetsmentioning

confidence: 99%

Tomato Leaf Diseases Classification using Convolutional Neural Networks with Transfer Learning Resnet-50

Muslih,

Krismawan

2024

KINETIK

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This research delves into the critical domain of Tomato Leaf Disease classification using advanced machine learning techniques. Specifically, a comparative evaluation was conducted between a Base CNN model devoid of ResNet-50 integration and a Proposed Method harnessing the capabilities of ResNet-50. The results elucidated a notable enhancement in performance metrics when leveraging ResNet-50, with the Proposed Method consistently achieving exceptional accuracy scores of 99.96%, 99.98%, and 99.96% across data splits of 90:10, 80:20, and 70:30, respectively. Furthermore, the ResNet-50 integration significantly augmented key metrics, including recall, precision, and F1-Score, thereby accentuating its pivotal role in enhancing sensitivity and positive predictive value for tomato leaf disease classification. As for prospective research trajectories, this study highlights potential avenues for refinement, encompassing the exploration of ensemble techniques amalgamating diverse architectural frameworks, advanced data augmentation methodologies, and broader disease classification scopes. Collectively, this research underscores the transformative potential of ResNet-50 in agricultural diagnostics, advocating for continued exploration and innovation to fortify global food security and sustainable farming practices. Future research could explore ensemble techniques, advanced data augmentation, broader disease classification scopes, and interdisciplinary collaborations to develop comprehensive diagnostic tools for sustainable farming practices and global food security.

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“…In this context, edge detection, image segmentation, and feature analysis are crucial techniques for recognizing and distinguishing cracks from other elements in road images [9]. Image detection methods such as the Sobel filter or thresholdbased segmentation algorithms can be employed to highlight suspicious areas as cracks [10], [11]. Moreover, the use of deep learning-based image processing has gained popularity in enhancing detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Road Crack Detection using Yolo-V5 and Adaptive Thresholding

Suhendar

2023

JAIS

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Road crack detection is a critical aspect of infrastructure maintenance, ensuring the safety and durability of roadways. This study presents an innovative approach leveraging image processing techniques, YOLO-V5 model, and adaptive thresholding for efficient and accurate road crack detection. The utilization of adaptive thresholding enables the system to handle complex lighting variations and diverse road textures, enhancing the precision of crack identification. Integrating the YOLO-V5 model further facilitates real-time detection and precise localization of road crack regions, contributing to effective and timely maintenance strategies. The research findings underscore the robustness and efficacy of the proposed methodology, emphasizing its potential for enhancing road safety and durability.

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Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review

Cited by 5 publications

References 129 publications

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Tomato Leaf Diseases Classification using Convolutional Neural Networks with Transfer Learning Resnet-50

Road Crack Detection using Yolo-V5 and Adaptive Thresholding

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