633 results about "Foreign object detection" patented technology

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

The invention discloses a power transmission line foreign matter detection method and system based on multi-modal image fusion, and relates to the technical field of intelligent operation and maintenance and state monitoring of a power system, a lightweight Ev-Mama architecture is introduced into a backbone network part of YOLOv13, the model keeps relatively low calculation complexity, and meanwhile, the power transmission line foreign matter detection efficiency is improved. And the modeling capability of the method on the long-range dependency relationship and the global semantic information is obviously enhanced. Besides, by using the CDIDF module, the EVCS module and the MHSAA module, on the basis of increasing a small amount of calculation, the scale sensing ability, the space structure modeling ability and the context understanding ability of the model are effectively improved, and the performance bottleneck of a traditional YOLO series network in the aspects of processing small targets, shielding targets and cross-scale information fusion is effectively relieved.

The invention discloses a liquid medicine foreign matter detection method and system based on machine vision, and the method comprises the steps: obtaining multiband spectral data of a liquid medicine sample, and carrying out the normalization processing to generate a liquid medicine background standard spectral feature template; comparing the refractive index spectral characteristics of the template and the foreign matter-containing liquid medicine, and recording the surface roughness scattering mode and polarized light reflection angle change data of a foreign matter area; calculating a spectrum peak displacement quantized value according to the polarized light data, and extracting foreign matter edge spectrum gradient information in combination with a threshold value; calculating a transparency spectral attenuation coefficient based on the edge gradient information, and obtaining spectral contrast enhancement factors of different foreign matters in combination with the spectral response characteristic data; and if the enhancement factor does not reach the standard, adjusting the spectrum correction parameter and recalculating, fusing the scattering mode and the multi-angle incident spectrum response result to carry out consistency verification, and obtaining a final identification result containing the foreign matter material type and the danger level. The method can improve the drug detection accuracy and guarantee the drug quality safety.

The invention relates to the technical field of wireless charging, and provides a foreign matter detection method and system for a wireless charging system and a storage medium. The method comprises the following steps: firstly, calibrating at multiple temperature points and establishing a temperature-parameter comparison table under the condition of no foreign matters; in a no-load standby state, the inversion module sends out detection drive and collects voltage, current and temperature; calculating a real-time quality factor and resonant frequency according to sampling, obtaining a compensation reference according to a temperature look-up table, and solving the variation of the quality factor and the resonant frequency; and performing fusion judgment by adopting dual-condition locking or weighted scoring, and triggering fault protection when a foreign matter is confirmed. Due to the fact that equivalent inductance and resistance changes are caused by metal foreign matters, quality factors are remarkably reduced along with resonant frequency deviation, temperature excursion and foreign matter effects are distinguished through temperature compensation, and dual-condition locking or weighted scoring fusion judgment is combined, the interference immunity and accuracy of detection are improved, false alarms and missing alarms are remarkably reduced, and the detection accuracy is improved. The method is suitable for high-power wireless charging scenes.

The invention belongs to the technical field of track detection, and more specifically relates to a track scene thunder-vision fusion anti-intrusion monitoring method and system based on sparse feature fusion. The method comprises the steps that a laser radar obtains three-dimensional point cloud data, a high-definition camera obtains visible light image data, and data preprocessing is carried out; performing external parameter automatic calibration optimization and time alignment compensation on the laser radar and the high-definition camera to obtain time-space aligned multi-modal data; fusing the camera semantic features and the laser radar geometric features based on multi-modal data of space-time alignment to obtain fused features; and after multi-modal feature fusion is completed, a target heat map is generated through a sparse detection head, and graded early warning is realized in combination with a dynamic safety distance model. The method solves the problems that an existing recognition algorithm is high in false alarm rate, and the false alarm rate is higher under low-light conditions such as rainy days or dusk; the data dependence is strong, and the ability to detect unlabeled novel foreign matters is almost zero.

A wireless power transmitter that transmits power to a wireless power receiver, the wireless power transmitter including a communication unit configured to communicate with the wireless power receiver; and a controller, wherein the communication unit receives a plurality of packets including a foreign object detection (FOD) status packet from the wireless power receiver, wherein the controller determines whether a foreign object is present in a charging area of the wireless power transmitter on the basis of the FOD status packet, wherein the communication unit transmits, to the wireless power receiver, a response signal indicating whether the foreign object is present in the charging area of the wireless power transmitter on the basis of a result of the determination, wherein the response signal is determined using a measured peak frequency of a power signal transmitted by the wireless power transmitter and a reference peak frequency included in the FOD status packet received from the wireless power receiver, and wherein the plurality of packets further include at least one of a signal strength packet, an end power transfer packet, a power control hold-off packet, a configuration packet, an identification packet for transmitting receiver identification information, an extended identification packet, a general request packet, a special request packet, a control error packet, a renegotiation packet, a 24-bit received power packet, an eight-bit received power packet, and a charging status packet.

The invention belongs to the technical field of foreign matter detection and alarm, and discloses a vehicle-mounted multi-sensor fusion runway FOD detection and alarm device. Comprising a data acquisition module for acquiring radar data, visual data and infrared data, and performing space-time synchronization and coordinate calibration to obtain synchronous data; the feature analysis module is used for analyzing the synchronous data to obtain radar features, visual features and infrared features; the analysis and complementation module is used for detecting the radar features, the visual features and the infrared features to obtain combined features, and performing shielding detection and feature complementation on the combined features to obtain complemented features; constructing an FOD dynamic feature tree; the positioning alarm module is used for analyzing the FOD dynamic feature tree and making an FOD alarm decision according to an alarm triggering condition; according to the method, accurate detection and reliable alarm of the runway FOD are realized, the accuracy, robustness and practicability of FOD detection in a complex scene are remarkably improved, and the runway FOD safety management and control requirements in a vehicle-mounted scene are effectively met.

The invention relates to the field of intelligent monitoring of electric power facilities, in particular to a power transmission line foreign matter detection method based on multi-component semantic segmentation and hybrid Transform-CNN (Convolutional Neural Network). According to the method, the Transform module is combined with the CNN model, multi-component semantic segmentation is performed on the to-be-analyzed image through the decoder based on the UNet network, and the method can be applied to rapid detection under complex conditions and numerous interference environments. Therefore, the foreign matter detection of the power transmission line for performing high-precision segmentation and real-time detection on the attached foreign matters on multiple parts such as a lead, an insulator and a fitting of the power transmission line in a complex scene is realized, the safety level alarm is performed when an abnormal trigger mechanism is reached, and the test shows that the IoU is improved by 15% and the false detection rate is reduced by 40% in rainy and foggy weather, so that the safety of the power transmission line is improved. The FPS reaches 32 frames per second (real-time edge calculation is met), the accuracy and robustness of foreign matter detection of the power transmission line are greatly improved, and the timeliness of safety early warning of the power transmission line is also greatly improved.

The application provides a foreign matter intrusion detection method and system for railway perimeter protection, relates to the technical field of railway perimeter protection, and first unifies the space-time reference of a millimeter wave radar, a visible light video and a fiber vibration signal based on a space-time calibration parameter, eliminates the physical space deviation between heterogeneous sensor data, and ensures the accurate alignment of multi-modal features under complex weather and terrain conditions;Secondly, the real-time reliability of each mode is dynamically evaluated through a data quality index, a deformable convolution offset field is generated combined with a cross-modal semantic correlation matrix, the feature space mapping relationship is adaptively adjusted, and the interference of video blur, radar noise and vibration false alarm is effectively suppressed;Finally, based on a lightweight model, the dynamically fused joint features are efficiently inferred, sub-meter level positioning accuracy of foreign matter detection is realized on an edge computing device, existing railway monitoring facilities are upgraded, maintenance cost is reduced, and an intelligent solution is provided for railway perimeter safety protection.

A computer-implemented system for foreign object detection in a scene. The system includes a memory-suppress diffusion network module adapted to reconstruct a reconstructed image from an encoded image, and a contrastive dissimilarity network adapted to combine the input image and the reconstructed image to predict an anomaly map for the input image. The encoded image is based on an input image, and the memory-suppress diffusion network module and the contrastive dissimilarity network are trained using only normal, real images. The system leverages only normal images in training and does not compromise the detection performance at the inference stage.

The embodiment of the invention provides a method for detecting foreign matters on a conveyor belt, and the method comprises the steps: obtaining a first image according to an image collection device in the transportation process of the conveyor belt; wherein the first image comprises visual information of the surface of the conveyor belt and objects on the conveyor belt; inputting the first image into a conveyor belt foreign matter detection model obtained by pre-training, and outputting a detection result corresponding to the first image; wherein the detection result comprises a second image, at least one target foreign matter name and confidence corresponding to the target foreign matter name, the second image comprises a detection frame used for framing the target foreign matter, and the conveyor belt foreign matter detection model at least comprises a first sub-module, a second sub-module and a third sub-module. According to the technical scheme, foreign matter detection is carried out on the conveyor belt based on the conveyor belt foreign matter detection model obtained through pre-training, the foreign matter on the conveyor belt can be rapidly and accurately recognized, and the effect of remarkably improving the detection efficiency is achieved.

The invention discloses a foreign matter detection method and device for a glass fiber cloth cover and a medium, and relates to the technical field of intelligent sensing, and the method comprises the steps: setting a transverse range based on a calibration value of scanning a transverse coverage width, extracting a minimum longitudinal coordinate and a maximum longitudinal coordinate of an update node, constructing a reconstruction point, and combining a reference difference to obtain a foreign matter detection result. A continuous foreign matter state field is constructed on the reconstruction points, a time window sequence is extracted based on the continuous foreign matter state field, an optimization objective function is constructed, iterative optimization is carried out, the final order and diffusion intensity of each reconstruction point are obtained, and a residual error is calculated; by introducing a time sequence judgment method based on fractional order kinetic parameter inversion, transient disturbance and real foreign matter events are effectively distinguished, the false alarm rate is reduced, and the stability of foreign matter type judgment is improved; and the space-time consistency and reliability of a foreign matter detection result under a high-speed production condition are improved.

A wireless charging device includes a first cover, a second cover, a foreign object detection component and a coil carrier tray. The foreign object detection component is disposed between the first cover and the second cover and has a through hole. The coil carrier tray is disposed between the first cover and the second cover, and includes a protrusion, wherein the protrusion passes through the through hole and abuts against the first cover.

A wireless power transmitter is configured to (i) detect a presence of a wireless power receiver that is couplable with the wireless power transmitter, (ii) after detecting the presence of the wireless power receiver, initiate wireless power transfer with the wireless power receiver, (iii) detect that a given amount of time has elapsed since a prior reference point, (iv) in response to detecting that the given amount of time has elapsed since the prior reference point, (a) pause the wireless power transfer with the wireless power receiver for a temporary period of time and (b) after the wireless power transfer is paused, perform a foreign object detection (FOD) process during the temporary period of time, and (v) after the FOD process is completed, resume the wireless power transfer with the wireless power receiver.

The invention relates to an airport foreign matter detection and cleaning robot, a control method thereof and a dual-redundancy device fault processing method, and aims to solve the technical problems that an existing airport foreign matter detection and cleaning robot is difficult to meet the detection-cleaning integrated requirement of an airport complex scene and is low in working efficiency. The invention provides an airport foreign matter detecting and cleaning robot. The airport foreign matter detecting and cleaning robot comprises a quadruped robot, a mechanical arm assembly, a visual assembly and a clamping jaw assembly. The clamping jaw assembly comprises a clamping jaw driving assembly, two in-place sensors, a clamping jaw and a supporting frame. The clamping jaw driving assembly comprises a metal shell, a clamping jaw control plate arranged in the metal shell, a driving plate and a motor, and an encoder is arranged on the motor; the support frame is arranged at the top of the metal shell through the in-place spring; the two in-place sensors are arranged on the two sides of the supporting face of the supporting frame respectively.

The invention discloses a bearing part intelligent detection and management system based on machine vision, and relates to the technical field of bearing manufacturing and detection. The method is used for solving the problems of bearing surface foreign matter recognition and pollution source tracking optimization. The system comprises a phase order correction and reconstruction module which generates a regularization weight factor through a high-frequency phase gradient, dynamically inhibits an order jump error of a raceway steep region, and outputs a high-precision three-dimensional shape and a gradient field; the coupling analysis module synchronously calculates local curvature entropy and polarization scattering intensity to realize foreign matter type identification and accurate positioning; the stress mapping decision module maps the foreign matter coordinates to a Hertz contact stress field, calculates a service life attenuation value in combination with a stress amplification coefficient, and generates a sorting priority decision map; and the pollution source reverse optimization module is used for dynamically adjusting dust removal equipment parameters based on foreign matter coordinate space clustering and assembly equipment position association. According to the system, closed-loop intelligent management of foreign matter detection and pollution source control is realized, and the bearing product quality and the production efficiency are improved.

The invention provides a cut tobacco foreign matter detection method and device, electronic equipment and a medium, and the method comprises the steps: carrying out the joint calibration of a depth image collection module and an optical image collection module, and collecting a first depth image and a first optical image at a first time point through the depth image collection module and the optical image collection module; according to depth information of each pixel point in the first depth image, extracting the first optical image to obtain a tobacco shred area image; and performing foreign matter detection on the tobacco shred area image by using a trained tobacco shred foreign matter detection model, wherein the trained tobacco shred foreign matter detection model comprises a space channel collaborative attention module. According to the method, the foreground and the background can be separated in a tobacco shred foreign matter detection scene, interference of the background on foreign matter detection is avoided, foreign matter detection can be carried out on the tobacco shred area image through the tobacco shred foreign matter detection model comprising the space channel collaborative attention module, and the foreign matter recognition accuracy can be improved.

The invention relates to the technical field of unmanned aerial vehicle charging, and discloses a high-endurance unmanned aerial vehicle charged by means of an exposed power transmission line and a charging control method thereof. The unmanned aerial vehicle comprises an unmanned aerial vehicle body, a clamping mechanism, a camera module, a battery module and a charging mechanism. The clamping mechanism is used for clamping a telegraph pole, the charging mechanism can make contact with a power transmission line for charging, an electric eye assembly is arranged in the charging mechanism to detect foreign matter, and the battery module provides power. The charging control method comprises the following steps: determining a charging demand according to the electric quantity of a battery, controlling an unmanned aerial vehicle to position and clamp a telegraph pole, enabling a charging mechanism to be in contact with a power transmission line for charging, performing foreign matter detection according to a set interval, detecting shaking of the power transmission line by using a camera module, and adjusting a charging state according to a detection result. And a fault line can be predicted and overhauled in combination with historical data. The unmanned aerial vehicle cruising charging problem is effectively solved, the charging convenience and the cruising ability are improved, line detection and fault early warning are achieved, and the power system management efficiency is improved.

This invention relates to a method and system for intelligent analysis of rooftop work behavior and foreign object detection in electric locomotives. The system includes maintenance personnel climbing onto the roof and inputting video stream data from a camera above the roof into a target detection module. The target detection module performs image preprocessing on the input video stream and then detects rooftop maintenance actions and safety ropes. Once the maintenance personnel are stationary, foreign object detection is performed on the roof. This invention assists operators of disconnecting switches in monitoring and analyzing the maintenance process on electric locomotive rooftops. It provides real-time monitoring and automatic alarms for work processes, actions, and tool status, reducing the risk of tool residue, standardizing work processes, and ensuring the safety of workers. This system represents a shift from traditional manual monitoring to intelligent and automated monitoring, providing strong technical support for rooftop work safety, tool management, and process traceability in electric locomotives.

The invention provides a power transmission line foreign matter detection method in a complex environment based on MEF-YOLOv8, and relates to the technical field of power transmission line intelligent detection.The method comprises the steps that firstly, a foreign matter image data set comprising bird nests, garbage, kite and balloons is constructed, the data set is expanded and enhanced through the Mosaic-9 data enhancement technology, and the foreign matter image data set is obtained; a LabelImg tool is adopted to carry out detection frame labeling on the data set, and the generalization ability of the model is improved; secondly, an efficient multi-scale attention (EMA) module is integrated into a C2f structure of a YOLOv8 backbone network, and pixel-level enhancement of multi-scale foreign matter features is achieved through feature grouping, parallel sub-network processing and cross-space learning; then, a Focal-EIOU loss function is integrated into the YOLOv8 model, and the training process is optimized through EIOU loss item calculation, a Focal weight adjustment mechanism and hyper-parameter lambda fusion; and finally, training and testing the improved YOLOv8 model, and outputting a detection result. According to the method, the detection precision and robustness of the foreign matters of the power transmission line in a complex environment are remarkably improved.

The invention discloses an orbit foreign matter detection method and system, and relates to the technical field of orbit detection.The method comprises the steps that an unknown foreign matter model is built through Gaussian distribution and a convex hull algorithm, and a known foreign matter model is built in combination with prior data; extracting a flat area of the ballast bed, projecting the foreign body model into a depth map and generating a shadow, and superposing to obtain a synthetic image with a mark; training a segmentation network by using the synthetic image and a real image, and obtaining a potential foreign matter mask through thermodynamic diagram binaryzation, connected domain analysis and size screening during reasoning; and inherent facility false alarms are eliminated through a secondary classification network, a real foreign matter detection result is finally output, alarm is triggered according to a detected threshold value, and efficient and accurate detection is realized. According to the method, on the premise that real foreign matter samples are scarce and ballast bed scenes are complex, efficient, accurate and real-time ballastless track foreign matter detection is achieved, meanwhile, the false alarm rate is reduced, and the generalization ability of the model to unknown foreign matter is improved.

The invention belongs to the technical field of nuclear power station weak and small target recognition, aims to solve the problem that an existing target detection method is difficult to effectively detect a high-speed falling foreign matter, and discloses a high-speed weak and small foreign matter detection method based on event and image fusion. A simulation data set is constructed to perform data enhancement, a physical model of a moving image and an event stream generation mechanism are utilized to construct a cross-modal feature fusion model, adaptive space-time alignment is performed on an event stream and a video frame image, and cross-modal data complementary enhancement fusion is performed on the event stream and the video frame image after space-time alignment. And training is carried out by using a target detection network, and feature extraction of a weak target fusing local and global attention is carried out. According to the method, the event and the image information are combined, and the instant reactivity of the event data and the detail retention of the image data are combined, so that the high-speed falling foreign matter can be effectively detected.

The application provides a wireless charging device and a foreign matter detection method, device, circuit and equipment thereof. The method comprises the following steps: applying a damping oscillation signal processing circuit to process a damping oscillation signal of a wireless power transmitter to be detected, so as to obtain a corresponding pulse signal; determining two target amplitudes of the damping oscillation signal and a target cycle number between the two target amplitudes according to the pulse signal; determining a current oscillation frequency and a current quality factor of the wireless power transmitter to be detected based on the two target amplitudes and the target cycle number; and determining whether there is foreign matter in the working range of the wireless power transmitter to be detected based on the current oscillation frequency, the current quality factor, a preset standard quality factor and a preset standard oscillation frequency. The application can output a pulse signal accurately reflecting the damping oscillation signal of the wireless power transmitter, so as to calculate the current quality factor and the current oscillation frequency of the wireless power transmitter based on the pulse signal, and realize more accurate foreign matter detection.

The invention provides an image foreign matter detection method capable of reducing false detection probability. The method comprises the following steps: searching a difference region between a to-be-detected image and a standard image; binarizing the image according to the difference region to obtain a binarized abnormal image; inputting a to-be-detected image into the false detection model, and marking false detection points and non-false detection points; binarizing the image according to the false detection points to obtain a binarized false detection image; searching a connected domain Ai and a connected domain Bj in the two binarized images respectively, wherein the connected domains are recorded as the connected domain Ai and the connected domain Bj; if the consistency of a certain connected domain Ai and any connected domain Bj meets the requirement, deleting the connected domain Ai; marking the connected domain Ai which is not deleted in the to-be-detected image as a foreign matter region; according to the method, a deep learning model classification method and an image processing method are fused, the robustness of the foreign matter detection method is enhanced, suspected areas are screened through fusion of the two methods, and the false detection rate of foreign matter detection is effectively reduced; the anti-interference capability is stronger, and the foreign matter area positioning is more accurate.

The invention provides a real-time foreign matter detection method and system based on multispectral imaging, and belongs to the technical field of computer vision and image processing.The method comprises the steps that multiband spectral images in the 400-1000 nm range of a target area are collected, noise sensitive bands are screened through a gray-level co-occurrence matrix, and targeted noise reduction is conducted; constructing a multispectral feature fusion network containing an attention mechanism to generate a fusion feature map; an improved YOLOv5 algorithm (including a feature pyramid attention module with an extrusion-excitation module) is adopted to detect foreign matters, and a result is output after post-processing. The system comprises a multispectral imaging module, a preprocessing module, a feature fusion module, a foreign matter detection module and a result output module. The foreign matter distinguishing capability is improved by utilizing the multispectral characteristic, real-time accurate detection is realized through targeted noise reduction, dynamic characteristic fusion and an optimization algorithm, and the method is suitable for the fields of industrial production, food processing and the like.

The invention discloses a reference frequency calibration and foreign matter detection method for a wireless charging system. The method comprises the following steps: firstly, acquiring coil initialization resonant frequency and recording a reference input current value in a no-load state of a transmitting end; then dynamically adjusting the reference resonant frequency through a pre-established compensation model based on the environment temperature and the input voltage; detecting the offset between the adjusted reference frequency and the actual resonant frequency and the input current offset in real time, and comparing the offset with a preset threshold value to judge the metal foreign matter; and for the condition that the frequency deviation is in the preset verification interval, the transmitting end and the receiving end complete communication protocol handshake and then are switched to the resonant working frequency, and charging is started, so that accurate calibration of the idle standby frequency and efficient identification of the foreign matter are realized.

The application belongs to the technical field of target detection, and provides a mine car track foreign matter detection method and system based on a deep neural network, which comprises the following steps: acquiring real-time images and text prompt words of a mine car track; extracting hierarchical multi-modal features of the acquired real-time images; obtaining image-text fusion features of the mine car track by using the hierarchical multi-modal features obtained through multi-modal deep neural network fusion and the text prompt words; detecting whether there is foreign matter in the mine car track according to the obtained image-text fusion features of the mine car track and a preset foreign matter detection model; and when there is foreign matter, identifying the size of the foreign matter and determining the position of the foreign matter, thereby completing real-time monitoring of foreign matter in the mine car track.

The invention discloses a foreign matter detection parameter calibration method and system for wireless charging. According to the foreign matter detection parameter calibration method for wireless charging, it is confirmed that a transmitting coil of a charger and a receiving coil of a device to be charged are adjusted to preset relative positions; adjusting the transmitting power of the transmitting coil at the preset relative position; power loss values are calculated to obtain the power loss values calibrated at the preset relative position under different transmitting power conditions, and the power loss values serve as foreign matter detection parameters. Thus, the accuracy and sensitivity of wireless charging foreign matter detection are improved through self-adaptive calibration of the power loss values under different transmitting power conditions in different coupling states, the false alarm rate is remarkably reduced, and therefore the user experience and the charging safety are improved; the problems of inaccurate detection, high false alarm rate, poor compatibility and the like caused by a fixed threshold value or lack of dynamic self-adaptive capability are effectively solved.

The invention relates to a railway yard foreign matter intrusion detection system and method based on infrared thermal imaging. The system comprises an intelligent scheduling system which is used for obtaining route information matched with an operation task under the condition that the railway yard has the operation task; the intelligent vehicle-mounted system is used for detecting a locomotive position matched with the route information; the foreign matter detection system is used for determining a driving front limit area of the locomotive based on the route information and the position of the locomotive and acquiring an infrared thermal imaging image corresponding to the driving front limit area; and sending alarm information to an intelligent vehicle-mounted system under the condition of determining that foreign matters exist in the driving front limit area based on the infrared thermal imaging image, so that the intelligent vehicle-mounted system controls the state of the locomotive based on the alarm information. By adopting the system provided by the invention, the foreign matter detection accuracy of the railway yard can be improved.

Estimating friendly metal losses (PFM) associated with power transfer from a wireless power transmitter (PTx) to a wireless power receiver (PRx) can include determining PFM based on one or more circuit parameters; subtracting an effect of one or more voltage or current measurements from the determined PFM to derive a modified PFM; and determining ecosystem scaling parameters based on the modified PFM. The ecosystem scaling parameters can include scale factors based on operation of the PTx with a reference PRx; operation of the PRx with a reference PTx; and / or operation of the reference PRx with the reference PTx. Estimating PFM can further include adding the effect of at least one of the one or more voltage or current measurements from the determined PFM to derive one or more loss coefficients; and performing foreign object detection based on a PFM model including the derived loss coefficients.