21 results about "Foreground detection" patented technology

The invention provides a joint training and image processing method, device and system, and relates to the fields of artificial intelligence, optical imaging and the like. The method comprises the steps that a training sample is acquired, the training sample comprises a training image and a real label, and the real label is a foreground detection result corresponding to the training image; obtaining a modulation result after the training image is modulated by the first optical assembly, and determining an input image according to the modulation result; inputting the input image into the neural network model to obtain an output foreground detection result, and determining the foreground detection result as a prediction label; obtaining a foreground enhancement evaluation result corresponding to the input image, and determining comprehensive loss according to the prediction label, the real label and the foreground enhancement evaluation result; and updating structural parameters of the first optical component and / or model parameters of the neural network model according to the comprehensive loss. By applying the scheme, the task processing precision and the like can be improved.

The invention discloses a method for realizing foreground multi-modal detection based on adaptive dynamic weight fusion, which comprises the steps of multi-modal data acquisition, multi-modal data preprocessing, multi-modal feature extraction, multi-modal feature fusion, foreground detection and prediction result post-processing. The multi-modal feature fusion step comprises the following steps: a) respectively inputting the extracted RGB features and IR features into a weight prediction sub-network, wherein the sub-network generates a spatial weight map for each modal; each pixel value in the weight map represents the local confidence degree of the mode for foreground discrimination at the corresponding spatial position; b) normalizing the spatial weight maps corresponding to the modalities to enable the sum of the weights of the modalities at the same spatial position to be 1; and c) multiplying the normalized spatial weight map by the corresponding modal features element by element, and adding the modal weighted feature maps element by element to obtain a final comprehensive feature map for foreground detection. According to the method, the foreground detection precision and robustness are remarkably improved.

The invention relates to the technical field of computer vision and image processing, in particular to a foreground detection method fusing wavelet transform and a prompt mechanism, which is characterized in that in a dynamic background, high-speed motion and small target scene, F-measure is averagely improved by more than or equal to 0.30, and the detection precision is obviously superior to that of FFNet and ASCNet. The wavelet transform attention module suppresses background noise, highlights target edges and textures, and improves input feature purity. The CFF, MFR and PB of the decoder collaboratively fuse multi-scale features, redundant information is reduced, and target details and contour integrity are reserved. According to integral end-to-end training, single-frame reasoning only needs 28 ms, and real-time performance and accuracy are both considered. Therefore, the problems that in an existing neural network foreground target detection algorithm, input data are often influenced by noise and background interference, redundant information is easily introduced into a decoder in the multi-level feature fusion and transmission process, and consequently the detection result is inaccurate are solved.

The invention discloses an X-ray chip cavity detection method and system based on a VIBE algorithm. The method comprises the following steps: constructing a background model according to a cavity analysis algorithm; collecting a sequence image of a chip to be detected, and initializing the background model by using a front preset frame number of the sequence image; determining an adaptive segmentation threshold according to the image background complexity, and performing foreground detection and segmentation according to the adaptive segmentation threshold; adjusting the dynamic update rate of the background model according to the image background complexity, and updating the background model according to the dynamic update rate; performing morphological closing operation on the obtained foreground binary image; then connected component analysis is carried out, statistics is carried out on the total pixel number of all connected areas, the ratio of the total pixel number to the total pixel number of the chip to be detected is calculated, and the hole proportion is obtained; according to the invention, the background model of each pixel point can be established by analyzing the image of the chip area. And then, comparing each pixel of the current image with the background model so as to separate the foreground in the image.

This invention belongs to the field of computer vision and spacecraft on-orbit servicing technology, specifically involving a lightweight two-stage component segmentation method for non-cooperative space targets. Step one: Constructing an edge-guided foreground detection network, employing a MobileNetV3 backbone network combined with lightweight structures such as Ghost modules and depthwise separable convolutions, and introducing an edge-guided attention module to output a spacecraft foreground mask; Step two: Fusing the RGB image with this foreground mask to generate foreground enhancement features, constructing a fine-grained segmentation network within the foreground constraint region, and using the same lightweight backbone network to improve segmentation accuracy; Step three: Employing a staged training strategy, setting differentiated loss functions and learning rates, calculating the loss in the foreground region, and completing model training. This invention, through its lightweight architecture and staged segmentation design, compresses the model parameter count to 4.2M, achieving an optimal balance between segmentation accuracy and lightweight efficiency, enabling real-time segmentation of key spacecraft components on a spaceborne edge platform.

The application discloses a dynamic scene background modeling and foreground detection method based on a box plot, which comprises the following steps: saving the obtained video images into a background queue according to a dynamic frame extraction speed; based on the current background queue state, removing a moving interference target by using a box plot statistical method, modeling the background after removing the interference target, and obtaining a background model; and performing difference operation on each frame of the obtained video images by using the background model to obtain a foreground; and experiments prove that the dynamic scene background modeling method based on the box plot can effectively improve the modeling efficiency and controllability, and has important significance in the actual dynamic scene foreground detection task.

This invention relates to a LOTO information-based and intelligent integrated security management method, comprising the following steps: 1) Optimizing the YOLO human body recognition algorithm under high-definition images, dividing the image into multiple regions with the same resolution as the standard image of the algorithm, and recognizing them separately; 2) Using OpenCV for image stitching, dividing, matching, and stitching these regional images so that the segmented object remains intact after the fusion of multiple images, avoiding duplicate recognition; 3) Adding motion foreground detection, using OpenCV for foreground detection; 4) Visual recognition of the number of locks; 5) Label recognition, using OCR to visually recognize security warning words; 6) Visual recognition of equipment status, developing algorithms for on-site equipment scenarios to identify the operating status of the equipment; This invention can reduce the risks caused by subjective factors of personnel in the traditional LOTO operation process, greatly improving LOTO security.

The application relates to the fields of computer vision and industrial intelligent detection technology, and discloses a foreground-background decoupling joint training method for an intelligent gangue selection target detection model. The method constructs an A background sample set, a B foreground sample set and a C real scene sample set; an initial target detection model containing a feature extraction layer, a feature fusion layer and a detection head is acquired, and initial weights are loaded; the A background sample set and the B foreground sample set are combined to form a foreground-background decoupling joint training set, and background sample identifiers or foreground sample identifiers are set for the training samples; in the first training process, a loss mask is generated based on sample type identifiers and target instance numbers, foreground detection loss and background suppression loss are respectively calculated, a weighted sum of the two is taken as a joint optimization target, and a foreground-background decoupling pre-training model is obtained; and then, taking the model as a starting point, scene fine-tuning is carried out on the C real scene sample set, and a target detection model is obtained.

This invention provides a method and apparatus for detecting abandoned objects. First, a background image of a detection area and multiple frames of images to be detected within the detection area are acquired. Each frame of the image to be detected and the background image are sequentially input into a foreground detection model for detection, resulting in the position region of each foreground object in each frame of the image to be detected. The position region is the area within the image to be detected where the bounding rectangle of each foreground object is located. For each foreground object, motion state detection is performed based on its position region to obtain the motion state detection result. Finally, for each foreground object, only if the detection result indicates that the foreground object remains stationary for a preset time is the foreground object determined to be an abandoned object. This method improves the detection accuracy of abandoned objects and avoids the influence of factors such as lighting and camera shake on detection accuracy.

The application provides a port pollution ship monitoring method and device, the method comprises the following steps: acquiring a video image of a port area shot by a UAV, the video image is composed of continuous video frames; using a mixed Gaussian background model to perform foreground detection on each video frame pixel by pixel to obtain a foreground image containing a potential area; performing optical flow calculation on each foreground pixel point in the foreground image to determine the motion information of each foreground pixel point; according to the motion speed data and the motion direction information in the motion information, screening abnormal pixel points from the foreground pixel points that meet a preset abnormal condition; performing connected domain analysis on the abnormal pixel points, and determining a pollution area and a pollution type according to the analysis result; based on the detection time and the spatial position of each pollution area, combining ship automatic identification system data, calculating the correlation probability of each pollution area and a candidate ship to determine a pollution ship. The pollution ship is traced, and the port pollution is monitored in a wide coverage and all-weather mode.

This invention belongs to the field of industrial anomaly detection based on computer vision, specifically involving a multi-task anomaly detection method based on target-oriented representation distillation and background denoising. The method includes constructing and training a multi-task anomaly detection model based on backdistillation, and using the trained multi-task anomaly detection network to detect anomalies in industrial product images. The multi-task anomaly detection model includes a teacher encoder, a student decoder, a single-class bottleneck embedding module, and a foreground detection module. This invention innovatively combines foreground detection with feature distillation, using target-oriented feature representation learning to solve the problem of mismatch between the pre-trained model and the distribution of industrial data.

The invention is suitable for the field of detection, and discloses a multi-moving-target tracking counting method, device and equipment and a storage medium, and the method comprises the steps: obtaining a current frame of an input image sequence according to a time sequence, carrying out foreground detection and morphological operation, carrying out connected domain analysis, obtaining a plurality of connected domains, and carrying out the foreground detection and morphological operation; screening out a connected domain of which the area is greater than a preset connected domain area threshold as a current frame moving target; when the bounding box size of the current frame moving target is greater than a preset bounding box splitting threshold, splitting the current frame moving target into at least two current frame sub-targets; according to the method, the current frame moving target and the current frame sub-target which are not split are collectively called as the current frame effective target, the state of the current frame effective target is updated through association, creation and removal mechanisms, and after the state updating of all the current frame effective targets is completed, the number of the current frame effective targets is counted. And each sub-target can be identified and tracked independently.

The present disclosure provides an image stabilization method and device, electronic equipment and storage medium, relates to the field of artificial intelligence, in particular to the technical field of computer vision, intelligent transportation, vehicle-road cooperation and the like. The specific implementation scheme is: detecting the current image foreground of a perception device to obtain information of each obstacle in the current image; for each obstacle, determining a corresponding target stabilization frame in each preset stabilization frame of the current image; determining whether the obstacle shields the corresponding target stabilization frame based on the information of the obstacle; when not shielding, calculating the offset between the corresponding target stabilization frame and the corresponding stabilization frame in the reference image to obtain the stabilization offset of the corresponding target stabilization frame of the obstacle; when the stabilization offset of the corresponding target stabilization frame of at least one obstacle does not exceed a preset threshold, determining the new position of each obstacle based on the stabilization offset of the corresponding target stabilization frame of at least one obstacle and the information of each obstacle, thereby realizing the stabilization of the perception device.

The invention discloses a micro-vibration monitoring method based on foreground detection and adaptive Euler video amplification, and belongs to the technical field of equipment micro-vibration monitoring, and the method comprises the steps: firstly collecting an electromechanical equipment operation video, extracting a foreground through a Gaussian mixture model to obtain a mask, and segmenting the mask into sub-regions through an SLIC algorithm; 4 vibration indexes of each sub-region are calculated and quantized intensity is fused, so that the number of layers of the pyramid and an amplification coefficient are matched; and building a Gaussian pyramid, a Laplacian pyramid and double IIR filtering to extract a target vibration signal, and reconstructing an image after amplifying and superposing an original signal according to parameters to obtain a vibration enhanced video. According to the micro-vibration monitoring method based on foreground detection and adaptive Euler video amplification, precise monitoring of micro-vibration of equipment is realized, adaptive matching of amplification parameters is realized, loss of details and artifacts are avoided, the identification degree of vibration signals is improved, meanwhile, the method adapts to a complex industrial environment, a clear vibration enhanced video is output, and the method is suitable for popularization and application. Faults of the assisting equipment can be found early, and operation and maintenance efficiency and safety are guaranteed.

This invention provides a method for detecting evidence left behind by a person and an integrated system for detecting such evidence. [Solution] The object detection method includes step A, which is a preprocessing step used for acquiring site images and foreground detection; step B, which is an object pairing and tracking step that detects objects in the original RGB images and selects objects that frequently appear in the monitored environment; and step C, which is an object detection step that detects whether an object is a common object at the site based on the object's overlap rate, displacement, and dwell time. This enables monitoring of whether objects at a site in a public space are artifacts.

The application discloses a foreground object detection method and device based on a Gaussian mixture model, and an electronic device. Through application of the technical solution of the application, on one hand, a judgment condition of original model matching can be modified by calculating active points in an image. Thus, by performing model matching only on pixel points with a large change in pixel value in the image, consumption of meaningless model matching in a video stream image can be reduced. On the other hand, the result of twice Gaussian model matching can indirectly complete the effect of image denoising, and this way will not cause small targets to be filtered out. Thus, the problem of large foreground detection overhead in related technologies can be avoided.

The application is suitable for the field of detection, and discloses a multi-motion target tracking counting method, device, equipment and storage medium.The method comprises the following steps: acquiring a current frame of an input image sequence in time sequence, performing foreground detection and morphological operation, then performing connected domain analysis to obtain multiple connected domains, and screening out connected domains with an area greater than a preset connected domain area threshold as current frame motion targets; when the size of the boundary box of the current frame motion target is greater than a preset boundary box splitting threshold, the current frame motion target is split into at least two current frame sub-targets; the current frame motion target and the current frame sub-targets that are not split are collectively referred to as current frame effective targets, the state of the current frame effective targets is updated through an association, creation and removal mechanism, and after the state updating of all the current frame effective targets is completed, the number of the current frame effective targets is counted.The method can split oversized targets, and ensures that each sub-target can be independently recognized and tracked.

The application discloses a kind of isolating switch opening and closing movement track monitoring method and device, the method comprises: obtaining the target movement track video of isolating switch opening and closing under light irradiation;Contrast of target movement track video is obtained using image technology adjustment light and shade, and multiple groups of contrast movement track video;Using foreground detection algorithm from each contrast movement track video corresponding motion feature sequence is extracted;According to each motion feature sequence, the connecting rod motion feature sequence of isolating switch opening and closing is determined;According to connecting rod motion feature sequence, the trajectory curve of isolating switch opening and closing is drawn;Contrast the trajectory curve of isolating switch opening and closing with the target trajectory curve of isolating switch opening and closing under standard working condition whether consistent;If consistent, then determine the movement track of isolating switch opening and closing is normal. Thus in the case of sufficient light, realize the movement track of isolating switch opening and closing is monitored automatically, and then effectively reduce manpower cost.

The application discloses a fruit and vegetable video foreground detection method based on adaptive updating parameters, and the method is characterized in that: through an iterative process, parameters of different hypergeometric distributions for different frames are given, and a normal equation set and a matrix form are given by making the first derivative of the weight of all hypergeometric distributions in a frame equal to zero through a square error weighted sum function, so as to obtain a matrix expression of the weight of different hypergeometric distributions in the frame, wherein the parameters of the hypergeometric distribution can be adaptively updated after a certain frame in an interval period, and then the size of the weight corresponding to different hypergeometric distributions is selected in turn for a certain pixel point in the frame in the fruit and vegetable video sequence, so that fruit and vegetable video foreground detection is completed.

The application discloses a kind of based on adaptive coding's cloud edge collaborative mobile target detection task's traffic optimization system and method, comprising: video frame acquisition;Metric network state;Foreground detection identifies region of interest;Collect historical inference result to obtain current data packet region of interest;Region of interest integration;According to video frame content complexity, region of interest content complexity and current network state, calculate global quantization parameter and region of interest quantization parameter offset;Encoding and transmission are carried out to data packet.The application can effectively maintain the quality of target area while reducing cloud edge collaborative traffic, thereby improving the accuracy of target detection.In addition, the method of the application effectively reduces the computational burden, and provides higher performance in real-time video processing scenarios such as edge computing.

The application discloses a kind of mould parts quality control systems, including camera module, image processing module and quality control module;Camera module is used to obtain the detection image of mould parts;Image processing module is used to judge whether mould parts pass quality inspection based on detection image, including the gray scale processing of detection image, obtains gray scale image;The foreground detection of gray scale image is obtained, and foreground image is obtained;After noise reduction processing to foreground image, judge whether mould parts pass quality inspection;Quality control module is used to the quantity of mould parts that do not pass quality inspection is counted, obtains statistical result, and is used to the quality control of mould parts based on statistical result.The application effectively reduces the number of pixel points that enters into noise reduction processing stage, to improve the efficiency of quality inspection to mould parts using machine vision, it is beneficial to quickly quality detection to mould parts.