Aug 2026· Journal of Computer Science· Vol 22, pp. 2411-2424· 0 citations· 36 references
TL;DR
A combination of enhanced CNN preprocessing and the YOLOv11 model to enhance the effectiveness of crack detection takes advantage of the capabilities of the YOLOv11 model to detect cracks on tiles and aims to use a wide range of tile images in different lighting conditions, textures, and types of defects.
Abstract
: The tile business is a critical part of the national economic development, as it is one of the areas that provides employment, produces goods, and exports them. Regardless of its significance, there are challenges in the industry that are caused by issues with production, which in most cases is caused by low-quality materials used or by mishandling of the products during transportation. Historically, visitors to the site have been able to detect tile cracks using human eyes, which, in addition to being expensive and time-consuming, can also be unreliable. This paper proposes a trustworthy approach to identifying tile cracks using the YOLOv11 model to solve these challenges. It is a combination of enhanced CNN preprocessing and the YOLOv11 model to enhance the effectiveness of crack detection. It takes advantage of the capabilities of the YOLOv11 model to detect cracks on tiles and aims to use a wide range of tile images in different lighting conditions, textures, and types of defects. The approach uses a powerful tile image analysis approach, and the high detection precision with the bounding box method is 88.30%, and the mask method precision is 88.77% with a small number of false positives.
A Yolov8-MED design integrate MHSA, ECA and DCNv3 to enhance the model’s ability to capture fine crack details and improve detection accuracy, to enhance the crack location accuracy and keep the computational cost simultaneously.
Mohammed Al-Soswa, Mohammed Al-Mahbashi, Zhaoyun Sun et al.· Computing· 0 citations
This project aims to develop a deep learning model utilizing Convolutional Neural Networks (CNNs) to detect and categorize surface defects in metal photos. Traditional manual inspection is typically slow, uneven, and full of mistakes, with a research gap in automated solutions. The objective is to provide an effective and dependable solution for fault identification, crucial in manufacturing and quality control procedures. A proprietary CNN will be trained on a large dataset of labeled pictures, facilitating the extraction of critical characteristics via convolutional, pooling, and fully connected layers. The project will examine the usage of pre-trained models such as VGG16 and ResNet50. These models will be evaluated singly and in combination using an ensemble method to boost classification accuracy and resilience. The models’ performances will be measured based on evolution metrics such as accuracy, loss, and their ability to categorize photos into one of the defect categories. It is predicted that the ensemble strategy would give greater performance compared to individual models, illustrating the usefulness of deep learning-based methods for automated fault identification. The method will give a scalable deep learning framework for defect detection, enabling real time integration in manufacturing industries.
Prottoy Dev, Aninda Chowdhury, Mohd Abdul Haleem Abir· Engineering· 0 citations
Biomass briquettes are increasingly recognized as a promising source of renewable energy, as they are produced from organic waste materials such as agricultural residues, wood scraps, and coconut shells. As an environmentally friendly energy alternative, ensuring their quality is essential to achieve consistent combustion and energy efficiency. Previous studies have explored the evaluation and classification of briquettes using image-based machine learning techniques. One study applied a Convolutional Neural Network (CNN) to distinguish good from bad briquettes, achieving very high performance, with accuracy, precision, and recall reportedly reaching up to 100%. However, these results were obtained using a dataset of briquette images captured on a plain white background with minimal variation. When tested on more complex data images of briquettes against non-uniform backgrounds containing multiple objects, the CNN model’s performance dropped drastically. Under these real-world conditions, accuracy and precision fell below 50%, indicating that the model lacked robustness for practical applications. To overcome this limitation, further research focused on developing an improved classification model using the Region-Based Convolutional Neural Network (R-CNN) method. R-CNN employs a region proposal algorithm using a selective search approach, enhancing object detection and localization in cluttered backgrounds. Experimental testing through a developed interface demonstrated a significant improvement, with accuracy increasing to approximately 75%. These results suggest that the R-CNN method provides a more reliable and practical solution for classifying briquette quality, particularly in the presence of complex image backgrounds, highlighting the importance of advanced object detection techniques for real-world applications.
Norbertus Tri Suswanto Saptadi, P. Chyan, Jonatan Sentani· JOIV: International Journal...· 0 citations
Corrosion poses significant challenges to steel structures, making early detection essential to avoid high maintenance costs and potential disasters. Automated detection and extent classification enable precise damage assessment, aiding targeted repair strategies and efficient resource allocation. Traditionally, human surveyors have conducted inspections, but these manual methods are labour-intensive and time-consuming and may yield inconsistent results. To overcome the conventional limitations, this study proposes applying image processing and a convolutional neural network (CNN) to corrosion detection, using a database of 9920 images captured with regular, portable cell phone cameras. The method uses 15 well-known pre-trained models (AlexNet, DenseNet-121, EfficientNet-B0, GoogleNet, Inception-v3, MobileNet-v1, NASNet, ResNet-18, ResNet-50, ResNet-101, ShuffleNet-v1, SqueezeNet, VGG16, VGG19 and Xception). CNN models are used to detect corrosion at ten levels, based on the percentage extent of corrosion, with levels ranging from 0% to 10% (level 1) and from 90% to 100% (level 10). After comparing 15 pre-trained models, Xception was chosen as the best, achieving a mean accuracy of 0.72, an accuracy of 0.93 and F1 and recall values of 0.91 and 0.94, respectively. This automated approach can aid early detection of corrosion, enhance maintenance prioritisation and reduce inspection costs for steel structures.
Unknown authors· Proceedings of the Instituti...· 0 citations
Welding helps in the joining of two materials using high temperature and filler material. The material's complete properties depend on the welding quality, which makes the product more resilient towards the strong factors. It is common to encounter faults in welded materials; therefore, to address this issue, a visual inspection of the product is carried out. The development of Artificial Intelligence enables the automated detection of weld defects. Deep learning models are mainly used for defect detection, especially of CNNs. This paper proposes an explanatory model for the classification process of the Vision Transformer using ResNet18 feature extraction on the welding surface images dataset. The proposed work, using patches and attention, achieves efficient classification, and Local Interpretable Model Explanation is used to explain the features responsible for the defect and to rectify it. By this method, the proposed work showed 92.96% accuracy and 92.82% F1 score.
Lakshmaiya N· 2026 7th International Confe...· 0 citations
This study demonstrates the feasibility of CNN-based brown rice defect detection, with future work directed toward lightweight deployment and multimodal fusion for production-line application.
Zhao You, Jianchun Yan, Hai Wei et al.· Foods· 0 citations
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