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Article
Engineering, Multidisciplinary
Bo Liu et al.
Summary: This paper proposes a deep learning method called low-pass U-Net to improve the segmentation effects of strip steel defects. The method combines low-pass filters and adaptive variance Gaussian low-pass layers to effectively perform defect detection and segmentation. The proposed method achieves considerable performance improvement in practical datasets.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Review
Materials Science, Textiles
Jinzhuang Xiao et al.
Summary: This paper proposes a high-precision convolutional neural network called tiny object-focused UNet for detecting towel defects. It introduces a coordinate attention mechanism and spatial pyramid pooling to enhance feature extraction capabilities and reduces the impact of defect-background imbalance on detection accuracy through a composite loss function.
TEXTILE RESEARCH JOURNAL
(2023)
Article
Automation & Control Systems
Zhiqiang Geng et al.
Summary: This research proposes a deep learning method that combines deep convolutional generative adversarial networks (DCGAN) and a seam carving algorithm to address the issue of small sample defect detection. The method is applied to the defect detection of water walls in an actual thermal power generation plant, achieving a detection accuracy of 98.43%, surpassing other methods and demonstrating the best generalization ability.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Hongfei Yang et al.
Summary: In this study, a novel method for detecting surface defects on rail tracks is proposed. The method allows for real-time, efficient, and reliable detection of surface defects. Experimental results demonstrate the strong performance, stability, and adaptability of the proposed method under different challenges.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Qiangqiang Lin et al.
Summary: This article proposes an edge and multi-scale reverse attention network (EMRA-Net) for tiny and low-contrast surface defect detection. Through feature extraction and fusion, the EMRA-Net outperforms existing methods and shows great potential in defect detection.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Chemistry, Analytical
Zexuan Guo et al.
Summary: With the development of artificial intelligence technology and the popularity of intelligent production projects, intelligent inspection systems have become a hot topic in the industrial field. This paper introduces the improved MSFT-YOLO model for object detection in the industry, which addresses challenges such as background interference and scale changes. The model achieves real-time detection and shows higher detection accuracy compared to baseline models, offering advantages and improvements.
Article
Engineering, Civil
Zhengxing Chen et al.
Summary: This paper proposes a multi-source data fusion algorithm for rail surface defect detection in both camera-based rail inspection images and ultrasound B-scan images. The algorithm utilizes image processing and feature extraction networks to achieve rail surface segmentation and feature extraction, and uses a feature fusion network to fuse the feature information from different data sources. Experimental results demonstrate that the algorithm achieves high accuracy in rail surface defect detection.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Jingpeng Wang et al.
Summary: Surface defect inspection of no-service rail is crucial for the safety of railway transportation. In this paper, a neural network called CLANet is proposed for defect inspection and accurate segmentation of rail surface. By integrating depth image and RGB image, the proposed method overcomes the challenges brought by irregular defect boundaries and similar foreground and background. A new cross-modal fusion strategy and dual stream decoder are introduced to enhance the detection performance. Furthermore, a new industrial dataset NEU RSDDS-AUG is built to address the scarcity of defective data. Experimental results demonstrate the effectiveness of the proposed method, which outperforms the state-of-the-art methods on various benchmark datasets.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Automation & Control Systems
Shuanlong Niu et al.
Summary: This article proposes a defect image generation method that uses a generative adversarial network to control the defect regions and strength. The method improves defect segmentation performance, particularly for small, weak defects.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Dehua Zhang et al.
Summary: This paper proposes a novel deep convolutional neural network algorithm for surface defect detection, which improves detection accuracy and speed by optimizing the network structure and introducing new techniques, and has outstanding generalization ability.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Zhong Qu et al.
Summary: In this article, a deeply supervised convolutional neural network with a novel multiscale convolutional feature fusion module is proposed for crack detection, providing improved accuracy and performance. Experimental results demonstrate that the method outperforms other state-of-the-art crack detection, edge detection, and image segmentation methods in terms of F1-score and mean IU.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Taiheng Liu et al.
Summary: This paper proposes an adaptive image segmentation network (AIS-Net) for pixelwise segmentation of surface defects, addressing the difficulties in surface defect detection. The proposed AIS-Net outperforms state-of-the-art approaches on actual surface defect datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Proceedings Paper
Acoustics
Jian Zhang et al.
Summary: This study proposes a network for real-time surface defect segmentation and improves segmentation accuracy by introducing auxiliary tasks and global context upsampling. Additionally, a mobile phone screen surface defect segmentation dataset is presented to facilitate research in this field.
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2022)
Article
Engineering, Electrical & Electronic
Zhengmei Lu et al.
Summary: This article presents a new set of measurement equations for the mixed three-phase three-wire and three-phase four-wire pattern in distribution systems. It also proposes a three-phase SE model that incorporates constraints and uses the exponentially weighted least squares method. The empirical results demonstrate the accuracy and effectiveness of the proposed formulation.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Erhu Zhang et al.
Summary: In this paper, an edge-guided and differential attention network (EGD-Net) is proposed for defect detection in industrial production. Experimental results show that the proposed method outperforms other methods, especially for complex background defects.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2022)
Article
Engineering, Electrical & Electronic
Jia Zeng et al.
Summary: This study proposes a novel network architecture called MINDS for gesture recognition, suitable for both sEMG and AUS modalities. Additionally, a cross modality knowledge distillation framework is introduced to improve the accuracy of sEMG by transferring knowledge from AUS. Experimental results demonstrate the superiority of MINDS over other networks under both sEMG and AUS modalities, confirming the effectiveness and feasibility of this approach.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Kaifeng Yang et al.
Summary: Surface defect detection is important in industrial production and directly impacts efficiency and quality. This article proposes a method called Ghost-SE light U-Net (GSLU-Net) for detecting heat sink surface defects. GSLU-Net combines lightweight convolution, self-attention, and fully convolutional network to reduce computation cost while maintaining high accuracy. By introducing the Ghost and squeeze-and-excitation (SE) modules, the network achieves improved efficiency and accuracy compared to previous FCNs. The experiments demonstrate the effectiveness and superiority of GSLU-Net.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Lemiao Yang et al.
Summary: This article proposes a scratch detection method combining deep learning and image segmentation algorithm, which has advantages in both accuracy and detection speed of scratch recognition, and can effectively segment scratch pixels.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Automation & Control Systems
Long Wen et al.
Summary: This article introduces a novel learning rate scheduler based on reinforcement learning (RL) for convolutional neural networks (RL-CNN) in fault classification, which can efficiently and automatically adjust the learning rate, outperforming traditional methods and showing potential in fault classification.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Construction & Building Technology
Dawei Li et al.
Summary: This work introduces a novel two-stage learning-based method for sewer pipe defect detection and fine-grained classification, leveraging multi-layer global feature fusion techniques. Through a strengthened region proposal network and integration of global contextual features, the method achieves state-of-the-art performance for sewer pipe inspection.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Engineering, Civil
Fan Yang et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Xuebin Qin et al.
PATTERN RECOGNITION
(2020)
Article
Automation & Control Systems
Hongwen Dong et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Proceedings Paper
Automation & Control Systems
Zhan Xinzi
2020 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE 2020)
(2020)
Article
Computer Science, Information Systems
Sen Wang et al.
Article
Computer Science, Information Systems
Xuefeng Li et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Zhe Wu et al.
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
(2019)
