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foreign object detection in the inspection of cloud server center using separable self-attention

INTELLIGENT SERVICE ROBOTICS 2025年第2期18卷 279-292页

作者： Jiang, Yang Li, Ziheng Zhao, Bin Zhang, Xuejiao Dong, Xuefeng Northeastern Univ Fac Robot Sci & Engn Shenyang 110169 Peoples R China Siasun Robot & Automat Co Ltd Shenyang 110168 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

Ensuring the stability and safety of cloud service centers stands as a paramount task in management. However, due to the prolonged operation and recurring maintenance and repairs, safety hazards are easily introduced. Meanwhile, research on foreign object detection in cloud server centers remains a relatively unexplored area. In this study, we designed a comprehensive strategy to address the foreign object detection task in cloud server centers. Firstly, we utilized patrol robots to collect data while proposing a fuzzy learning annotation strategy to accommodate the diversity of foreign objects found in real-world scenarios. Subsequently, we introduced a method for augmenting negative samples to address the problem of inadequate network fitting. By enhancing the network's capacity to learn from negative samples, we ultimately achieved a perfect detection accuracy rate of 100% for the negative samples. Eventually, based on Faster R-CNN, our research introduced an improved foreign object detection network for reliable and real-time extraction of foreign objects from background scenes. The network presented herein innovatively employs a separable self-attention mechanism to achieve an enhanced refinement of the ConvMixer. This enables our model to learn global features, capture long-range dependencies within a sequence, focus on salient regional features, and generate high-quality regions of interest (ROI). Ultimately, the method proposed in this study achieved a mean average precision (mAP) of 98.45% on the foreign object dataset from cloud server centers. This holds practical significance in ensuring the stable operation of cloud server centers.

关键词： Cloud server centers foreign object detection Attention mechanism Negative sample enhancement Faster R-CNN

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foreign object detection on vibrating screens: A precision-oriented rotational detection framework with attention mechanisms

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MEASUREMENT 2025年 250卷

作者： Wang, Weidong Zhao, Xuan Song, Yang Fan, Yuhan Cui, Yao Wu, Yuxin Li, Jiangtao Zeng, Hongjiu Lv, Ziqi China Univ Min & Technol Beijing Sch Chem & Environm Engn Beijing Peoples R China China Univ Min & Technol Beijing Sch Artificial Intelligence Beijing 100083 Peoples R China China Univ Min & Technol Beijing Inner Mongolia Res Inst Ordos 017001 Peoples R China Yulin Energy Co Ltd Natl Energy Grp Washing Ctr Yulin Shanxi Peoples R China

foreign objects in coal mining and washing operations pose significant challenges, including equipment wear, production inefficiencies, and safety hazards. Current sorting methods, predominantly manual or based on Horizontal Bounding Box detection, struggle to meet the requirements of dynamic environments due to their inability to accurately predict target orientation and suppress background interference. This study introduces YOLOv5-SROD, a rotational object detection algorithm tailored for foreign object detection on vibrating screens. The model introduces rotating bounding boxes with a Circular Smooth Label strategy, ensuring stable and accurate angle predictions while addressing challenges such as angle jumping. Additionally, the Squeeze-andExcitation attention mechanism enhances feature extraction in complex scenarios by suppressing noise from reflective water spray and high-glare conditions. Experimental results reveal that YOLOv5-SROD achieves a mAP@0.5 of 84.5%, processes at 30.4 FPS, and features a lightweight design with 21.68 million parameters, outperforming both HBB methods and state-of-the-art rotational detection models. These results highlight YOLOv5-SROD's capability to deliver real-time, accurate detection in challenging industrial environments, offering a scalable and practical solution for foreign object detection in coal preparation processes.

关键词： foreign object detection Rotational object detection Attention mechanism Real-time detection Vibrating screens

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foreign object detection for transmission lines based on Swin Transformer V2 and YOLOX

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VISUAL COMPUTER 2024年第5期40卷 3003-3021页

作者： Tang, Chaoli Dong, Huiyuan Huang, Yourui Han, Tao Fang, Mingshuai Fu, Jiahao Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Peoples R China West Anhui Univ Sch Elect & Elect Engn Luan 237012 Peoples R China

Suspended foreign objects on transmission lines will shorten the discharge distance, easily leading to phase-to-ground or phase-to-phase short circuits, which induces outage accidents. foreign objects are small and difficult to identify, resulting in low detection accuracy. An improved foreign object detection method based on Swin Transformer V2 and YOLOX (ST2Rep-YOLOX) is proposed. First, the feature extraction layer ST2CSP constructed by Swin Transformer V2 is used in the original backbone network to extract global and local features. Secondly, hybrid spatial pyramid pooling (HSPP) is designed to enlarge the receptive field and retain more feature information. Then, Re-param VGG block (RepVGGBlock) is introduced to replace all 3 x 3 convolutions in the network to deepen the network and improve feature extraction capabilities. Finally, experiments are carried out on the transmission lines foreign object image dataset, which was obtained using unmanned aerial vehicles (UAVs). The experimental results show that the average accuracy of the ST2Rep-YOLOX method can reach 96.7%, which is 4.4% higher than that of YOLOX. The accuracy of the nest, kite, and balloon increased by 9.3%, 15.4%, and 9.6%, and the recall increased by 6.5%, 9.4%, and 2.5%, respectively. This method has high detection accuracy, which provides an important reference for foreign object detection in transmission lines.

关键词： Transmission lines foreign object detection YOLOX Swin Transformer V2 RepVGGBlock

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foreign object detection in urban rail transit based on deep differentiation segmentation neural network

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HELIYON 2024年第17期10卷 e37072页

作者： Tan, Feigang Zhai, Min Zhai, Cong Shenzhen Inst Informat Technol Sch Traff & Environm Shenzhen 518172 Peoples R China South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510640 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Foshan Univ Sch Transportat & Civil Engn & Architecture Foshan 528225 Peoples R China

With the increasing scale of urban rail transit, foreign object intrusion has become a significant operational safety hazard in urban rail transit. Although the laser-based automatic foreign object detection system has advantages such as long-distance detection and insensitivity to light changes, it has drawbacks such as large blind spots and low visualization. In response to the problems existing in laser detection systems, we proposed a novel video-based deep differentiation segmentation neural network for foreign object detection. Firstly, the foreign object detection is transformed into a binary classification problem, and the foreign object is determined as the image's foreground using image segmentation principles. Secondly, build a deep segmentation network based on deep convolution. Finally, perform morphological operations and threshold judgment on the foreground segmentation image to filter out the final detection results. To improve the detection effect, we reduced the impact of airflow disturbance by sampling and calculating the average background image. At the same time, the channel attention model and spatial attention model are added to the deep differentiation neural network. Collecting real data on subway platforms for experiments shows that the proposed method has a detection accuracy of 95.8 %, which is superior to traditional detection methods and recent image segmentation neural networks.

关键词： Differential network foreign object detection Deep neural network Seam foreign object

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foreign object detection in Aqueous Food Media using Surface Electric Potential and Machine Learning Techniques 39

Foreign Object Detection in Aqueous Food Media using Surface...

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39th International Conference on Image and Vision Computing New Zealand

作者： Lee, Kin Wai Hayes, Michael Heffernan, Bill Bones, Phil Fourie, Jaco Univ Canterbury Elect & Comp Engn Christchurch New Zealand Univ Canterbury Elect Power Engn Ctr Christchurch New Zealand Lincoln Agritech Christchurch New Zealand

ISBN: (纸本)9798331518783;9798331518776

foreign object contamination remains a problem in food processing despite the existing countermeasures to detect and remove unintended objects during the food production process. Most imaging techniques only work well in detecting specific objects but may not be suitable in more challenging problem settings, such as objects with different shapes, conductivities, and densities in aqueous food media. This paper introduces a new approach for detecting objects embedded in aqueous food media using surface potential measurements obtained from a three-dimensional cylindrical sensing pipe and convolutional neural network (CNN) classifiers. The simulated electric potential measurements from the surface of the sensing pipe are constructed as two-dimensional images, providing essential information for the CNN classifier to infer the presence of objects and to distinguish opposing conductivities based on distinctive spatial characteristics. Experimental results evaluated on unseen object shapes and orientations suggest that surface potential measurements with low noise interference and CNN classifiers can be used to reliably detect the presence of objects and classify the conductivity of the objects.

关键词： foreign object detection electric potential sensing convolutional neural network non-invasive diagnostics food safety

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foreign object detection in Wireless Power Transfer Systems

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第1期58卷 1340-1354页

作者： Lu, Jianghua Zhu, Guorong Mi, Chunting Chris Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA

The successful commercialization of wireless power transfer (WPT) technology in mobile phones, laptops, and electric vehicles requires a safe, reliable, and efficient charging infrastructure. A metal object, such as a coin, a key, or a nail, however, may be inadvertently exposed, by close proximity, to the magnetic fields generated by the WPT coupler, creating a potential safety hazard and affecting the electrical characteristics of the WPT system. In addition, the magnetic fields may produce adverse symptoms, such as dizziness and breathlessness, in living objects near the charging systems. This article provides a current state of the art review of all available designs for detecting metal and living objects in WPT systems. Working principles and qualitative comparisons in terms of sensitivity, accuracy, and cost of major detection methods are presented to reveal their inherent limitations and available applications. Finally, a number of research topics on foreign object detection are suggested to encourage more researchers to make contributions to the development and commercial application of wireless charging systems.

关键词： Metals Coils object detection Temperature sensors Magnetic hysteresis Couplers Cameras foreign object detection living object protection metal object detection wireless power transfer

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foreign object detection for railway ballastless trackbeds: A semisupervised learning method

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MEASUREMENT 2022年 190卷

作者： Chen, Zhengxing Wang, Qihang Yu, Tianle Zhang, Min Liu, Qibin Yao, Jidong Wu, Yanhua Wang, Ping He, Qing Southwest Jiaotong Univ Key Lab High Speed Railway Engn Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China Shanghai Dongfang Maritime Engn Technol Co Ltd 88 Xiangyun Rd Shanghai 200011 Peoples R China China Railway First Survey & Design Inst Grp Ltd 2 Xiying Rd Xian 710043 Peoples R China

This paper proposes a semisupervised algorithm for detecting foreign objects in ballastless beds based on the improved deep SVDD (Support Vector Data Description) algorithm. First, we use the improved Mask R-CNN algorithm to extract the rail and fastener areas in images, assuming that no foreign object exists in the rail and fastener areas. Second, we deepen the backbone network of the deep SVDD to enhance its ability to extract deep semantics from complex images. We perform pure color coverage processing with different colors and mean blur processing with different blur kernels on the rails and fastener regions extracted by the improved Mask R-CNN. The results show that the AUC (Area Under the Curve) of our improved deep SVDD algorithm is 89.23% and improves the AUC compared to that of the original model by 11.09%.

关键词： foreign object detection Ballastless trackbed Semisupervised learning Deep learning Convolutional neural networks

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A Dense Attention Railway foreign object detection Algorithm Based on Mask R-CNN

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IEEE ACCESS 2024年 12卷 85761-85772页

作者： Gao, Shuang Hohhot Vocat Coll Railway Dept Hohhot 010010 Peoples R China

In railway safety monitoring, foreign object detection is a key task, especially in infrared and low illumination conditions. In order to improve the detection accuracy of railway foreign bodies, an optimized railway foreign bodies detection algorithm named O-Mask R-CNN is proposed in this study. Firstly, by integrating the densely connected feature pyramid network (FPN) and the convolutional attention mechanism (CBAM),the recognition ability of low-contrast objects and the detection accuracy of small-size foreign objects are significantly improved. In addition, O-Mask R-CNN uses an improved Zone Suggestion Network (RPN) and ROIAlign layer to ensure feature alignment, thereby optimizing the precision of the bounding box and the quality of the instance segmentation. Finally, a method of adjusting the size of anchor frame based on cluster analysis is introduced to adapt to the characteristics of different scale railway foreign bodies. After only 46 iterations, the developed railway foreign item identification algorithm achieved a constant total loss value and has strong iterative performance. Mean square error and mean absolute error are the smallest, 1.35 and 1.21 respectively, and the detection error performance is also very good. Finally, the real average detection accuracy of the algorithm on infrared and common railway foreign body photos is tested, which is up to 98.26 and 98.85, respectively. Therefore, the detection method created in this paper not only has high performance, but also provides technical support for foreign object recognition in practical application environment.

关键词： Attention mechanism convolutional neural networks mask foreign object detection railway night

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A foreign object detection method based on a laminated coil for wireless power transfer

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2025年第2期39卷 121-139页

作者： Guo, Caixia Li, Yangyang Guo, Hu Zhang, Zhijun Henan Normal Univ Coll Elect & Elect Engn Xinxiang 453007 Peoples R China Hennan Key Lab Optoelect Sensing Integrated Applic Xinxiang Peoples R China

foreign objects in magnetically coupled resonant wireless power transfer (MCR-WPT) systems cause eddy effects, reducing efficiency and raising the temperature. To address this, a high-frequency multilayer detection coil and composite topology circuit are designed to enhance sensitivity by dynamically adjusting component parameters. Verified through Maxwell simulation, the proposed method improves detection efficiency by 46% and eliminates blind spots compared to square coils. The detection range expands by 66% with a $ 3 \times 3 $ 3x3 reverse series-laminated coil, effectively decoupling sub-coils and broadening coverage. Experimental results show accurate detection of various foreign objects, such as iron and copper, by analyzing changes in magnetic fields and detection coil voltage. This approach enhances MCR-WPT system safety and efficiency, making it suitable for complex environments.

关键词： foreign object detection laminated coil compound topological circuit wireless charging

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A visual foreign object detection system for wireless charging of electric vehicles

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MACHINE VISION AND APPLICATIONS 2024年第4期35卷 69-69页

作者： Nejad, Bijan Shahbaz Roch, Peter Handte, Marcus Marron, Pedro Jose Univ Duisburg Essen Networked Embedded Syst Schutzenbahn 70 D-45127 Essen Germany

Wireless charging of electric vehicles can be achieved by installing a transmitter coil into the ground and a receiver coil at the underbody of a vehicle. In order to charge efficiently, accurate alignment of the charging components must be accomplished, which can be achieved with a camera-based positioning system. Due to an air gap between both charging components, foreign objects can interfere with the charging process and pose potential hazards to the environment. Various foreign object detection systems have been developed with the motivation to increase the safety of wireless charging. In this paper, we propose a foreign object detection technique that utilizes the integrated camera of an embedded positioning system. Due to operation in an outdoor environment, we cannot determine the types of objects that may occur in advance. Accordingly, our approach achieves object-type independence by learning the features of the charging surface, to then classify anomalous regions as foreign objects. To examine the capability of detecting foreign objects, we evaluate our approach by conducting experiments with images depicting known and unknown object types. For the experiments, we use an image dataset recorded by a positioning camera of an operating wireless charging station in an outdoor environment, which we published alongside our research. As a benchmark system, we employ YOLOv8 (Jocher et al. in Ultralytics YOLO, 2023), a state-of-the-art neural network that has been used in various contexts for foreign object detection. While we acknowledge the performance of YOLOv8 for known object types, our approach achieves up to 18% higher precision and 46% higher detection success for unknown objects.

关键词： Computer vision Electric vehicles Wireless charging foreign object detection Machine learning

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