Xin Wen^*, Xiao Zheng, Yu He

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4371-4388, 2025, DOI:10.32604/cmc.2025.060661 - 06 March 2025

Abstract Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation. However, existing detection methods often struggle with challenges such as complex defect morphology, texture similarity, and fuzzy edges, leading to poor accuracy and missed detections. In order to resolve these problems, we propose MSCM-Net (Multi-Scale Cross-Modal Network), a multiscale cross-modal framework focused on detecting rail surface defects. MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps, effectively capturing and enhancing features at different scales for each modality. To… More >

Thinh D. Le, Duong Q. Nguyen, Phuong D. Nguyen, H. Nguyen-Xuan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1459-1479, 2025, DOI:10.32604/cmes.2024.056753 - 27 January 2025

Abstract Research on reconstructing imperfect faces is a challenging task. In this study, we explore a data-driven approach using a pre-trained MICA (MetrIC fAce) model combined with 3D printing to address this challenge. We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction. Our results demonstrate high accuracy, evaluated by the geometric loss function and various statistical measures. To showcase the effectiveness of the approach, we used 3D printing to create a model that covers facial wounds. The More >

Syed Tasnimul Karim Ayon^1,#, Farhan Md. Siraj^1,#, Jia Uddin^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 499-520, 2025, DOI:10.32604/cmc.2025.058361 - 03 January 2025

Abstract This study investigates the application of Learnable Memory Vision Transformers (LMViT) for detecting metal surface flaws, comparing their performance with traditional CNNs, specifically ResNet18 and ResNet50, as well as other transformer-based models including Token to Token ViT, ViT without memory, and Parallel ViT. Leveraging a widely-used steel surface defect dataset, the research applies data augmentation and t-distributed stochastic neighbor embedding (t-SNE) to enhance feature extraction and understanding. These techniques mitigated overfitting, stabilized training, and improved generalization capabilities. The LMViT model achieved a test accuracy of 97.22%, significantly outperforming ResNet18 (88.89%) and ResNet50 (88.90%), as well… More >

Tajmal Hussain, Jongwon Seok^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 231-250, 2025, DOI:10.32604/cmes.2024.056621 - 17 December 2024

Abstract Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence (AI) and internet of things (IoT) to enhance efficiency, reduce costs, and ensure product quality. In light of the recent advancement of Industry 4.0, identifying defects has become important for ensuring the quality of products during the manufacturing process. In this research, we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network (CNN) architectures: VGG16, VGG19, Xception, and Mobile-Net V2, compensating for their… More >

Yuanyuan Wang^1,2,*, Jialong Huang¹, Md Sharid Kayes Dipu¹, Hu Zhao³, Shangbing Gao^1,2, Haiyan Zhang^1,2, Pinrong Lv¹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4973-4995, 2024, DOI:10.32604/cmc.2024.055839 - 12 September 2024

Abstract Printed circuit boards (PCBs) provide stable connections between electronic components. However, defective printed circuit boards may cause the entire equipment system to malfunction, resulting in incalculable losses. Therefore, it is crucial to detect defective printed circuit boards during the generation process. Traditional detection methods have low accuracy in detecting subtle defects in complex background environments. In order to improve the detection accuracy of surface defects on industrial printed circuit boards, this paper proposes a residual large kernel network based on YOLOv5 (You Only Look Once version 5) for PCBs surface defect detection, called YOLO-RLC (You… More >

Wanrun Li^1,2,3,*, Wenhai Zhao¹, Tongtong Wang¹, Yongfeng Du^1,2,3

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 553-575, 2024, DOI:10.32604/sdhm.2024.050751 - 19 July 2024

Abstract The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage, impacting the aerodynamic performance of the blades. To address the challenge of detecting and quantifying surface defects on wind turbine blades, a blade surface defect detection and quantification method based on an improved Deeplabv3+ deep learning model is proposed. Firstly, an improved method for wind turbine blade surface defect detection, utilizing Mobilenetv2 as the backbone feature extraction network, is proposed based on an original Deeplabv3+ deep learning model to address the issue of limited robustness. Secondly, through integrating the concept of… More > Graphic Abstract

Xiao Lu^1,*, Chengling Jiang¹, Zhoujun Ma¹, Haitao Li², Yuexin Liu²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 373-390, 2024, DOI:10.32604/cmc.2024.047469 - 25 April 2024

Abstract Insulator defect detection plays a vital role in maintaining the secure operation of power systems. To address the issues of the difficulty of detecting small objects and missing objects due to the small scale, variable scale, and fuzzy edge morphology of insulator defects, we construct an insulator dataset with 1600 samples containing flashovers and breakages. Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed. Firstly, a high-resolution feature map is introduced and a small object prediction layer is added so that the model can detect… More >

Kechen Song^1,2,*, Wenqi Cui², Han Yu¹, Xingjie Li¹, Yunhui Yan^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3953-3969, 2024, DOI:10.32604/cmc.2024.048451 - 26 March 2024

Abstract Segment Anything Model (SAM) is a cutting-edge model that has shown impressive performance in general object segmentation. The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model. Due to its superior performance in general object segmentation, it quickly gained attention and interest. This makes SAM particularly attractive in industrial surface defect segmentation, especially for complex industrial scenes with limited training data. However, its segmentation ability for specific industrial scenes remains unknown. Therefore, in this work, we select three representative and complex industrial surface defect detection scenarios, namely strip steel More >

Xinyu Hu, Defeng Kong^*, Xiyang Liu, Junwei Zhang, Daode Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 915-933, 2024, DOI:10.32604/cmc.2023.046376 - 30 January 2024

Abstract Printed Circuit Board (PCB) surface tiny defect detection is a difficult task in the integrated circuit industry, especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks. To improve the performance of PCB surface tiny defects detection, a PCB tiny defects detection model based on an improved attention residual network (YOLOX-AttResNet) is proposed. First, the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet (Squeeze and Excitation Network) attention… More >

Yongzhi Min^1,2,*, Jiafeng Li³, Yaxing Li¹

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 941-962, 2023, DOI:10.32604/cmc.2023.041182 - 31 October 2023

Abstract To guarantee the safety of railway operations, the swift detection of rail surface defects becomes imperative. Traditional methods of manual inspection and conventional nondestructive testing prove inefficient, especially when scaling to extensive railway networks. Moreover, the unpredictable and intricate nature of defect edge shapes further complicates detection efforts. Addressing these challenges, this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network (UPerNet) tailored for rail surface defect detection. Notably, the Swin Transformer Tiny version (Swin-T) network, underpinned by the Transformer architecture, is employed for adept feature extraction. This approach capitalizes on the global… More >