Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Information Systems
Yongbin Guo, Xinjian Kang, Junfeng Li, Yuanxun Yang
Summary: Facing detection problems caused by complex textile texture backgrounds, different sizes, and different types of defects, commonly used object detection networks have limitations in handling target sizes and weaker stability and anti-jamming capabilities. To meet the stringent requirements of textile defect detection, we propose a novel AC-YOLOv5-based method that fully considers the optical properties, texture distribution, imaging properties, and detection requirements specific to textiles.
Article
Engineering, Electrical & Electronic
Xin Xiang, Zenghui Wang, Jun Zhang, Yi Xia, Peng Chen, Bing Wang
Summary: Surface defect detection is crucial in steel production. Attention mechanisms are used to ensure quality, but they struggle to differentiate steel surface and natural images. To address this, we propose an adaptive graph channel attention (AGCA) module that incorporates graph convolutional theory. AGCA treats each channel as a feature vertex and uses adjacency matrices to represent their relationship. The non-local operations performed using AGCA's constructed graphs significantly enhance feature representation. Experimental results demonstrate AGCA's superiority over existing methods when integrated into defect detection networks. The code is available at https://github.com/C1nDeRainBo0M/AGCA.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Abraham Montoya Obeso, Jenny Benois-Pineau, Mireya Sarai Garcia Vazquez, Alejandro Alvaro Ramirez Acosta
Summary: This paper investigates attention mechanisms in Deep Neural Networks (DNNs) and proposes a method that incorporates human visual attention. The proposed approach achieves faster convergence and better performance in image classification tasks compared to global and local automatic attention mechanisms.
PATTERN RECOGNITION
(2022)
Article
Automation & Control Systems
Linhao Shao, Erhu Zhang, Jinghong Duan, Qiurui Ma
Summary: MPA-Net is a novel network that utilizes multi-scale cascade pyramid features and a guided context attention mechanism to detect surface defects at the pixel level. It integrates multi-scale features and can generate defect segmentation maps that can locate defects of different sizes and poor visibility.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Environmental Sciences
Loucif Hebbache, Dariush Amirkhani, Mohand Said Allili, Nadir Hammouche, Jean-Francois Lapointe
Summary: This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. The model combines pyramidal feature extraction and attention through a one-stage concrete defect detection model. The attention module extracts local and global saliency features, enhancing the localization of small and low-contrast defects, as well as the overall accuracy of detection in varying image acquisition ranges. Experimental results demonstrate the superior performance of SMDD-Net compared to state-of-the-art techniques.
Article
Engineering, Electrical & Electronic
Pengwen Lu, Junfeng Jing, Yanqing Huang
Summary: Surface defect detection is crucial in industrial production. In this study, we propose a network called MRD-Net for real-time and end-to-end defect segmentation. The network utilizes a multiscale feature enhancement fusion module, a reverse attention module, and a boundary refinement module to address challenges in generalizability, accuracy, and real-time detection. Experimental results demonstrate that MRD-Net outperforms state-of-the-art methods in terms of generalizability, accuracy, and achieves real-time detection.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Information Systems
Faquan Chen, Miaolei Deng, Hui Gao, Xiaoya Yang, Dexian Zhang
Summary: Surface defect detection is crucial for ensuring the quality of metallic products. In this paper, a metallic surface defect detector named AP-Net is proposed, which consists of a lightweight adaptive attention module (LAA) and an enhanced feature pyramid module (EFP). Experimental results demonstrate that the detection accuracies of representative detectors can be significantly improved using the LAA and EFP.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Shuo Wang, Hongyu Wang, Fan Yang, Fei Liu, Long Zeng
Summary: This paper proposes an object detection network combining attention with YOLOV4 for tiny defect detection. By constructing a chip-surface-defect dataset and training on it, the network achieves better performance compared to other methods.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Wenguan Wang, Jianbing Shen, Jianwen Xie, Ming-Ming Cheng, Haibin Ling, Ali Borji
Summary: This research focuses on predicting visual attention in dynamic scenes, introducing a new benchmark DHF1K and a novel video saliency model ACLNet. Through extensive evaluation on multiple datasets and analysis of saliency models, ACLNet shows superior performance and fast processing speed.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Neurosciences
Yash Sawant, Jogendra Nath Kundu, Venkatesh Babu Radhakrishnan, Devarajan Sridharan
Summary: In this study, a biologically inspired recurrent neural network (RNN) model (st-RNN) is proposed, which efficiently detects changes in natural images by simulating the circuit architecture of a midbrain attention network. Compared to conventional RNNs, the st-RNN learns faster, has fewer connections, and can reproduce key experimental phenomena and accurately predict human gaze fixations.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Ali Furkan Kamanli
Summary: Surface defect detection is crucial in industrial processes for ensuring product quality and reducing material waste. This paper proposes a novel approach called MCPAD-UNet for steel defect segmentation, which overcomes the limitations of traditional methods by incorporating multi-scale cross-patch attention and dilated convolution. Extensive testing on a public dataset demonstrates the effectiveness of the proposed method, achieving a Dice score of 95.3% and outperforming the competition by 5.2%. This approach has the potential to significantly improve defect detection in industrial processes, leading to reduced material waste and improved product quality.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Hongbing Shang, Chuang Sun, Jinxin Liu, Xuefeng Chen, Ruqiang Yan
Summary: Surface defect detection plays a crucial role in intelligent manufacturing and product life-cycle management. Existing methods mainly rely on convolutional architectures, but the limited receptive field poses challenges for performance improvement. Transformer-based models, with their ability to model long-range dependencies, have achieved success in computer vision. However, using Transformer models without modification lacks defect awareness and relevance.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Chemistry, Multidisciplinary
Wenbo Zhu, Quan Wang, Lufeng Luo, Yunzhi Zhang, Qinghua Lu, Wei-Chang Yeh, Jiancheng Liang
Summary: This paper proposes a new attention mechanism (CPAM) to address the issue of regional bias in tile block defect detection. By dividing feature information into patches, CPAM can successfully distinguish different regional features and linearly connect these patches in two spatial directions, thereby improving the performance of the model.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Multidisciplinary
Chao Zhao, Xin Shu, Xi Yan, Xin Zuo, Feng Zhu
Summary: Steel surfaces may have defects due to imperfect manufacturing techniques and external factors, which significantly impact their lifespan and usability. Therefore, surface defect detection is crucial in industrial production. However, traditional detection algorithms suffer from low accuracy and speed. In this study, we propose a model called RDD-YOLO, which is based on YOLOv5, for steel surface defect detection. Our model utilizes Res2Net blocks for feature extraction, a double feature pyramid network for enhanced representations, and a decoupled head for improved detection precision. Experimental results demonstrate that RDD-YOLO achieves accuracies of 81.1 mAP on NEU-DET and 75.2 mAP on GC10-DET, surpassing YOLOv5 by 4.3% and 5.8% respectively. Our proposed model shows comprehensive performance in steel surface defect detection.
Article
Computer Science, Interdisciplinary Applications
Francesco Pistolesi, Michele Baldassini, Beatrice Lazzerini
Summary: More than one in four workers worldwide suffer from back pain, resulting in the loss of 264 million work days annually. In the U.S., it costs $50 billion in healthcare expenses each year, rising up to $100 billion when accounting for decreased productivity and lost wages. The impending Industry 5.0 revolution emphasizes worker well-being and their rights, such as privacy, autonomy, and human dignity. This paper proposes a privacy-preserving artificial intelligence system that monitors the posture of assembly line workers. The system accurately assesses upper-body and lower-body postures while respecting privacy, enabling the detection of harmful posture habits and reducing the likelihood of musculoskeletal disorders.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Xavier Boucher, Camilo Murillo Coba, Damien Lamy
Summary: This paper explores the new business strategies of digital servitization and smart PSS delivery, and develops conceptual prototypes of smart PSS value offers for early stages of the design process. It presents the development and experimentation of a modelling language and toolkit, and applies it to the design of a smart PSS in the field of heating appliances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Dieudonne Tchuente, Jerry Lonlac, Bernard Kamsu-Foguem
Summary: Artificial Intelligence (AI) is becoming increasingly important in various sectors of society. However, the black box nature of most AI techniques such as Machine Learning (ML) hinders their practical application. This has led to the emergence of Explainable artificial intelligence (XAI), which aims to provide AI-based decision-making processes and outcomes that are easily understood, interpreted, and justified by humans. While there has been a significant amount of research on XAI, there is currently a lack of studies on its practical applications. To address this research gap, this article proposes a comprehensive review of the business applications of XAI and a six-step framework to improve its implementation and adoption by practitioners.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Francois-Alexandre Tremblay, Audrey Durand, Michael Morin, Philippe Marier, Jonathan Gaudreault
Summary: Continuous high-frequency wood drying, integrated with a traditional wood finishing line, improves the value of lumber by correcting moisture content piece by piece. Using reinforcement learning for continuous drying operation policies outperforms current industry methods and remains robust to sudden disturbances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Luyao Xia, Jianfeng Lu, Yuqian Lu, Wentao Gao, Yuhang Fan, Yuhao Xu, Hao Zhang
Summary: Efficient assembly sequence planning is crucial for enhancing production efficiency, ensuring product quality, and meeting market demands. This study proposes a dynamic graph learning algorithm called assembly-oriented graph attention sequence (A-GASeq), which optimizes the assembly graph structure to guide the search for optimal assembly sequences. The algorithm demonstrates superiority and broad utility in real-world scenarios.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Mutahar Safdar, Padma Polash Paul, Guy Lamouche, Gentry Wood, Max Zimmermann, Florian Hannesen, Christophe Bescond, Priti Wanjara, Yaoyao Fiona Zhao
Summary: Metal-based additive manufacturing can achieve fully dense metallic components, and the application of machine learning in this field has been growing rapidly. However, there is a lack of framework to manage these machine learning models and guidance on the fundamental requirements for a cross-disciplinary platform to support process-based machine learning models in industrial metal AM.
COMPUTERS IN INDUSTRY
(2024)