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IEEE 9th International Conference on Power Electronics Systems and Applications (PESA)

作者： Xiao, Jing Guo, Xiaoxuan Shi, Haonan Bai, Pengfei Gong, Wenlan Tang, Chunsen Southern Power Grid Corp Wireless Power Transmiss Joint Lab Guangzhou Peoples R China Guangxi Power Grid Co Ltd Elect Power Res Inst Nanning Peoples R China Chongqing Univ Coll Automat Chongqing Peoples R China

ISBN: (纸本)9781665486682

A non-blind-zone foreign object detection (FOD) scheme in Wireless Power Transfer (WPT) system is proposed, based on the impedance characteristics of the detection coil with double-layer structure. In this scheme, a double-layer coil is designed through the partition of the detection coil to achieve the uniformity of the distribution of the magnetic field, so as to improve the detection accuracy and eliminate the blind zone on the certain size of metal foreign object. Then, through the analysis of experiments, the sensitivity and the stability of metal FOD system are verified. It is shown that the designed system can detect the metal foreign object as small as a paper clip without blind zone whether the strong power magnetic field exists or not.

关键词： non-blind-zone foreign object detection wireless power transfer double-layer coil

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Camera-Adaptive foreign object detection for Coal Conveyor Belts

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APPLIED SCIENCES-BASEL 2025年第9期15卷

作者： Peng, Furong Hao, Kangjiang Lu, Xuan Shanxi Univ Inst Big Data Sci & Ind Taiyuan 030006 Peoples R China Shanxi Univ Coll Phys & Elect Engn Taiyuan 030006 Peoples R China

foreign object detection on coal mine conveyor belts is crucial for ensuring operational safety and efficiency. However, applying deep learning to this task is challenging due to variations in camera perspectives, which alter the appearance of foreign objects and their surrounding environment, thereby hindering model generalization. Despite these viewpoint changes, certain core characteristics of foreign objects remain consistent. Specifically, (1) foreign objects must be located on the conveyor belt, and (2) their surroundings are predominantly coal, rather than other objects. To leverage these stable features, we propose the Camera-Adaptive foreign object detection (CAFOD) model, designed to improve cross-camera generalization. CAFOD incorporates three main strategies: (1) Multi-View Data Augmentation (MVDA) simulates viewpoint variations during training, enabling the model to learn robust, viewpoint-invariant features;(2) Context Feature Perception (CFP) integrates local coal background information to reduce false detections outside the conveyor belt;and (3) Conveyor Belt Area Loss (CBAL) enforces explicit attention to the conveyor belt region, minimizing background interference. We evaluate CAFOD on a dataset collected from real coal mines using three distinct cameras. Experimental results demonstrate that CAFOD outperforms State-of-the-Art object detection methods, achieving superior accuracy and robustness across varying camera perspectives.

关键词： coal mine safety foreign object detection camera adaptability

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Integrated Solution for Electric Vehicle and foreign object detection in the Application of Dynamic Inductive Power Transfer

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第11期70卷 11365-11377页

作者： Shi, Wenli Dong, Jianning Soeiro, Thiago Batista Bauer, Pavol Delft Univ Technol DCE & S EEMCS NL-2624 CP Delft Netherlands

One of the challenges with the dynamic inductive power transfer (DIPT) technique is the electric vehicle detection (EVD) that helps the DIPT system to control the power supply of the transmitter. The EVD method applying auxiliary coils is a promising candidate because the flat shape of the auxiliary coils is suitable for the compact design. However, the EVD may fail when the metallic foreign object (MFO) is present. Therefore, the desire emerges in the integration design of the EVD and foreign object detection (FOD). The FOD can ensure the reliability of the EVD as well as the highly efficient operation of the DIPT system without MFOs. In this context, this paper proposes an integrated solution to the EVD and FOD well suited for DIPT systems. The integrated solution utilizes both passive coil sets (PCSs) and active coil sets (ACSs). Additionally, a novel detection resonant circuit (DRC) is proposed to realize EVD and FOD using the same coil sets and to amplify the measurement sensitivity. The operation mechanisms, the detection coil sets architecture, the design of the proposed resonant circuits and the detection procedure are detailed. Finally, a printed circuit board based prototype is built to validate the integrated functionality of the EVD and FOD in a DIPT prototype processing 1 kW output. Experiments considering the practical DIPT application scenarios are conducted, and the proposed detection method is able to achieve advantageously high sensitivity and no blind zone.

关键词： Coils Magnetic resonance Sensitivity RLC circuits Couplings Voltage measurement Voltage Dynamic inductive power transfer resonant circuits electric vehicle detection foreign object detection

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Numerical and Experimental Investigation of Time-Domain-Reflectometry-Based Sensors for foreign object detection in Wireless Power Transfer Systems

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SENSORS 2023年第23期23卷 9425-9425页

作者： Helwig, Martin Xu, Yun Hentschel, Uwe Winkler, Anja Modler, Niels Tech Univ Dresden Inst Leichtbau & Kunststofftech D-01307 Dresden Germany

foreign object detection (FOD) is considered a key method for detecting objects in the air gap of a wireless charging system that could pose a risk due to strong inductive heating. This paper describes a novel method for the detection of metallic objects utilizing the principle of electric time domain reflectometry. Through an analytical, numerical and experimental investigation, two key parameters for the design of transmission lines are identified and investigated with respect to the specific constraints of inductive power transfer. For this purpose, a transient electromagnetic simulation model is established to obtain and compare the sensor impedance and reflection coefficients with experimental data. The measurement setup is based on parametrically designed sensors in laboratory scale, using an EUR 2 coin as an exemplary test object. Consequently, the proposed simulation model has been successfully validated in this study, providing a comprehensive quantitative and qualitative analysis of the major transmission line design parameters for such applications.

关键词： electric time domain reflectometry wireless power transfer wireless charging inductive power transfer foreign object detection metal object detection

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A Raisin foreign object Target detection Method Based on Improved YOLOv8

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APPLIED SCIENCES-BASEL 2024年第16期14卷 7295页

作者： Ning, Meng Ma, Hongrui Wang, Yuqian Cai, Liyang Chen, Yiliang Jiangnan Univ Sch Mech Engn Wuxi 214122 Peoples R China Jiangsu Key Lab Adv Food Mfg Equipment & Technol Wuxi 214122 Peoples R China

During the drying and processing of raisins, the presence of foreign matter such as fruit stems, branches, stones, and plastics is a common issue. To address this, we propose an enhanced real-time detection approach leveraging an improved YOLOv8 model. This novel method integrates the multi-head self-attention mechanism (MHSA) from BoTNet into YOLOv8's backbone. In the model's neck layer, selected C2f modules have been strategically replaced with RFAConv modules. The model also adopts an EIoU loss function in place of the original CIoU. Our experiments reveal that the refined YOLOv8 boasts a precision of 94.5%, a recall rate of 89.9%, and an F1-score of 0.921, with a mAP reaching 96.2% at the 0.5 IoU threshold and 81.5% across the 0.5-0.95 IoU range. For this model, comprising 13,177,692 parameters, the average time required for detecting each image on a GPU is 7.8 milliseconds. In contrast to several prevalent models of today, our enhanced model excels in mAP0.5 and demonstrates superiority in F1-score, parameter economy, computational efficiency, and speed. This study conclusively validates the capability of our improved YOLOv8 model to execute real-time foreign object detection on raisin production lines with high efficacy.

关键词： raisins foreign object detection YOLOv8 computer vision

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foreign object detection of Sintering Transport Belt Based on CNN

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IFAC-PapersOnLine 2021年第21期54卷 25-30页

作者： CK. Xiao B. Sun YL. Wang LD. Qiu School of Information Science and Engineering Central South University Changsha China

Belt transportation is the most important transportation method in sintering production. Sintered products are often mixed with large gangue, ironware (grate bar, steel plate) leading to belt scratches, material plugging. In order to eliminate the influence of foreign objects on sintering production, this paper studies the detection of sinter foreign objects. The detection, classification and location of foreign objects are based on CNN, and non-maximum suppression is improved by considering the difference of image features between anchors. The detection model is proposed and the results indicate the effectiveness of the method.

关键词： Steel sintering Belt transportation foreign object detection NMS

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Improved YOLOv8n for foreign-object detection in Power Transmission Lines

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IEEE ACCESS 2024年 12卷 121433-121440页

作者： Wang, Hanjun Luo, Shiyu Wang, Qun Univ Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110000 Peoples R China

Effective and accurate detection of foreign objects in transmission lines plays a crucial role in achieving intelligent power inspection. However, in the real world, detecting objects that are too far or too small can lead to inaccurate object detection tasks. Therefore, this article proposes an improved model based on YOLOv8n to improve detection performance. We introduce attention mechanism into the YOLOv8n network and add a small object detection module to improve detection accuracy. Considering the requirements of detection tasks for detection speed and accuracy, after comparing the three attention mechanisms of CBAM, ECA, and GAM, we chose the backbone network formed by the fusion of YOLOv8n and ECA attention mechanism, and added a small object detection module in the head section. The results show that compared to the unimproved YOLOv8n model, this method can effectively improve detection accuracy and still perform excellently in detection speed and robustness.

关键词： Power transmission lines Accuracy Training Inspection Feature extraction Deep learning object detection YOLO Transmission line foreign object detection YOLOv8n ECA small object detection

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A foreign object detection Method Based on Variation of Quality Factor of detection Coil at Multi-frequency 12

A Foreign Object Detection Method Based on Variation of Qual...

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12th IEEE Energy Conversion Congress and Exposition-Asia (ECCE-Asia)

作者： Sun, Ying Wei, Guo Qian, Kejun He, Peng Zhu, Chunbo Song, Kai Harbin Inst Technol Harbin Peoples R China State Grid Jiangsu Elect Power Co Ltd Suzhou Peoples R China

ISBN: (纸本)9781728163444

Due to the contactless feature of the WPT (Wireless Power Transfer) system, the metal foreign objects could enter the coupling charging area possibly and he exposed in the high power alternating magnetic field. Heated by the eddy current, the metal foreign objects would ignite the inflammable materials and cause fire which has become the most potential safety hazard of WPT system. To solve this problem, this paper proposed a foreign object detection (FOD) method based on the quality factor variation of the detection coil at multi-frequencies. The proposed method could calculate the quality factor of the detection coil in real time through measuring the resonant capacitance voltage during the CA power transmission. Compared to traditional FOD methods, the proposed method could utilize the resistance variation of detection coil reasonably and preliminary realize the material identification of metal foreign object. By configuring different excitation frequencies the method could accurately identify the ferromagnetic metal foreign object which has the hazard of sharp temperature rise to WPT system. A 3.3kW FOD system prototype, which satisfied the standard SAEJ2954, was established to verify its feasibility. The experiment results showed that the error between the calculated quality factor of the proposed method and the actual measurement of instrument was less than 10% during power transmission. And the ferromagnetic and non-ferromagnetic metal foreign object with same size could be distinguished.

关键词： foreign object detection Quality Factor detection Coil Wireless Power Transfer

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A Rack Coil for Metal foreign object detection in WPT System 47

A Rack Coil for Metal Foreign Object Detection in WPT System

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Qi, Chao Duan, Hongyu Guo, Junwei Sun, Tian Wang, Wenwu Yang, Funing Harbin Inst Technol Sch Elect Engn & Automat Harbin Peoples R China

ISBN: (纸本)9781665435543

For metal foreign object detection (MFOD) in wireless power transfer (WPT) system, the electromagnetic induction coil detection method has the advantages of simple structure, strong environmental adaptability, and low cost. Whether it is a passive detection scheme or an active detection scheme, the detection effect is closely related to the mutual inductance between the detection coil and the metal foreign object. Thus, the structure of the detection coil needs to be optimized It is very meaningful to optimize the structure of the detection coil for the purpose of improving mutual inductance. In this paper, a rack-shaped detection coil is designed. Firstly, theoretical analysis is used to compare the mutual inductance between the single-turn rack and the circular metal foreign object and the mutual inductance between the rectangular detection coil and the circular metal foreign object. Secondly, simulation analysis is used to compare the mutual inductance between the rack coil pair and the metal foreign object and the mutual inductance between the rectangular coil pair and the metal foreign object. Finally, experiment shows that the single-layer rack coil can effectively detect 1 RMB coin and double-layer rack coil can accurately locate the coin on the basis of effective detection when the WPT system power is 1.5kW and the vertical distance between the detection coil and the primary coil is 15mm. 1 jiao RMB coin is the minimum size of the object that can be detected accurately by the detection system.

关键词： WPT rack coil foreign object detection

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foreign object detection for Wireless Power Transfer Based on Machine Learning

Foreign Object Detection for Wireless Power Transfer Based o...

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IEEE Wireless Power Transfer Conference (WPTC)

作者： Ote, Masaya Jeong, Soyeon Tentzeris, Manos M. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781728142388

A simple foreign object detection (FOD) method utilizing a neural network is discussed. For a receiver and transmitter structure, open helical types of coil resonating at 13.56 MHz were used with different distances and with and without the presence of foreign objects (a copper plate or plastic bottle filled with water). An FOD system was constructed based on a neural network that detects foreign objects based only on reflection coefficient (S11) parameters. This approach for FOD achieved an accuracy of approximately 98% in the range 12-18 cm.

关键词： Wireless power transfer WPT magnetic resonance foreign object detection FOD machine learning neural network

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