82 results about "Object detector" patented technology

Techniques for determining a presence of an object, especially an object such as animal or debris, in a path of a vehicle, are discussed herein. For example, sensors of various modalities, which may include multispectral sensors, may capture data representing an environment the vehicle is traversing. In examples, one or more trained machine learned (ML) models, operating on a vehicle computing system, may detect and / or classify objects in the environment, based on input data of one or more modalities or spectral bands. The ML models may be pre-trained using training data including real sensor data, synthetic data, and / or augmented data, along with auto-generated annotations. In some examples, hyperspectral data may be used to identify materials associated with detected objects. A confidence score associated with the detection of the object may also be computed. The vehicle may be controlled based on detection of the object and its classification.

The invention belongs to the field of computer vision and artificial intelligence security, and discloses a self-adaptive frequency domain adversarial training method and device for a target detector, the target detector comprises a repair module and a pedestrian detector which are connected in series, and the input of the repair module is connected with the output of a patch detector; the self-adaptive frequency domain adversarial training method comprises the following steps: losses in joint training comprise standard target detection losses, repair consistency losses on a frequency domain based on a frequency domain image corresponding to a training image and a clean image, and repair dependence losses based on a detected average precision mean value; according to the invention, the end-to-end joint training is carried out through the restoration module and the subsequent pedestrian detector, and the optimization target of the restoration module is directly aligned with the improvement of the detection robustness, so that the confrontation disturbance is eliminated as far as possible, and meanwhile, the key semantic information of the detection task is reserved to the maximum extent. The separation of the performance of the repair module and the pedestrian detector is avoided, and the detection robustness is improved.

Systems and methods for a multi-camera object detector having two or more backbone models. In particular, systems and methods are provided for including two or more backbone machine learning models, with one backbone optimized for speed and the other backbone optimized for precision. In particular, the first backbone can be highly accurate but slower than the second backbone and with a higher computer resource usage. The second backbone can be fast and efficient but have lower accuracy for object detection. In some examples, the second backbone can use fewer images and / or lower resolution images. The determination of which backbone to use can be based on fixed rules, or it can be determined based on another machine learning component. The outputs from the first and second backbones for each camera can be combined together into a unified representation, such as a bird's eye view (BEV) space.

Systems, methods, and storage media for manifesting a virtual object in a virtual environment are disclosed. Exemplary embodiments may: receive, at a first physical object detector, a first signal, from a first physical object-associated element in a first physical environment; identify, at a first value identification module, based on the first signal, a first value associated with the first signal; identify, at a first virtual object identification module, based on the first value, a first virtual object; and manifest, at a first virtual environment output device, a first manifestation of the first virtual object in a first manifestation of the first virtual environment.

An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.

An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.

An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.

An image capturing apparatus includes an image capturing unit, a driving unit configured to drive the image capturing unit, and at least one processor. The at least one processor is configured to function as a motion vector detector configured to detect motion vectors based on image data output from the image capturing unit, an object detector configured to detect a plurality of moving objects based on the motion vectors, and a controlling unit configured to perform tracking control by controlling the driving unit. The controlling unit calculates respective evaluation values of the plurality of moving objects based on at least one of (a) information on the plurality of moving objects, (b) information on a shake of the image capturing apparatus, and (c) information on a driving state of the driving unit. The controlling unit controls the driving unit based on the evaluation values.

A headlamp device includes a headlamp, an object detector, and a controller. The headlamp is mounted on a vehicle and includes a light emitting unit composed of multiple light emitting cells. The object detector detects an object around the vehicle and generates an object detection signal including object coordinates corresponding to a location of the detected object. The controller controls light of the headlamp based on information on the object coordinates. The controller may control the light emitting unit such that at least some light emitting cells are turned off according to an object detection signal. Each of multiple light emitting units includes a circuit board, the multiple light emitting cells separated from each other on the circuit board, a molding member formed between the light emitting structures, and a protective member formed on the molding member to surround sides of the multiple wavelength conversion members while filling a gap between the multiple wavelength conversion members.

A driving assistance device includes: a vehicle surrounding image aquisitor acquiring a captured image of surroundings of a vehicle or by processing the captured image, as a vehicle surrounding image where the surroundings of the vehicle are visually recognized from a set viewpoint; an object detector detecting an object of attention being an object present around the vehicle requiring attention; a model image acquisitor acquiring a model image indicating an appearance of the object of attention; an assistance image generator generating an assistance image where the model image is synthesized with the vehicle surrounding image according to a position of the object of attention; and an image display displaying the assistance image on a display device such that the vehicle surrounding image excluding the model image has a luminance difference or a contrast ratio with the model image increasing stepwise as a distance from the viewpoint increases.

A power transmitter (101) provides wireless power to a power receiver (105). The power transmitter (101) comprises a transmitter coil (103) generating a power transfer signal. A communicator (307) is arranged to communicate using a communication carrier and an object detector (is arranged to detect objects. An update circuit (315) receives (701) a power receiver identity modulated on communication carrier by a power receiver in response a detection of the power receiver. The identity is stored and transmitted to an update device by modulation of the communication carrier when the update device is detected. Update data modulated on the communication carrier by the update device is received and stored with a link to the identity. When a power receiver is subsequently detected, a new identity is received (711) and update data linked to this identity is retrieved (713). The retrieved update data is then transmitted to the power receiver which may then perform an update using the update data.

The present application relates to a kind of method and system for detecting general object based on dynamic inference network.The method comprises: based on the given general object detector, construct the dynamic inference object detector of multiple outlet;For the dynamic inference object detector of multiple outlet, insert multi-scale adaptive gating network;Using the training strategy of no hyperparameter, train the dynamic inference object detector of multiple outlet and the multi-scale adaptive gating network;Using the dynamic inference object detector of multiple outlet and the multi-scale adaptive gating network of training completion, using variable time delay inference strategy carries out general object detection.The present application can be widely applied in the deployment of a variety of general object detector, and then be applied to intelligent security, automatic driving, unmanned aerial vehicle survey and a variety of application scenarios, to realize the effective deployment of the same model in a variety of hardware requirements, application, reduce the consumption of manpower, material resources and financial resources.

A host satellite comprises a first memory segment for storing a mission operation lookup table having at least one mission operation identifier associated with mission parameter constraints, a target object type and object detection model parameters associated with the target object type. A first sensor provides satellite sensor data that represents orbital mission characteristics. A second sensor captures images. A mission operation selector is responsive to the orbital mission characteristics and the mission parameter constraints for selecting the at least one mission operation identifier. An object detector is responsive to the captured images and the object detection model parameters associated with the selected mission operation identifier for detecting objects from the captured images. An analytics generator is responsive to the detected objects, the orbital mission characteristics, and the mission operation identifier for creating a mission analytics packet. A communication interface transfers the mission analytics packet to the client terminal.

The present invention relates to a computer-implemented method of training an object detector (OD), an object detector (OD), a computer program and a computer-readable (storage) medium. In order to carry out the method, a set of object classes (C) obfuscated by a neural network (NN), an object feature map (FM) originating from the neural network (NN), and object class tags (LO) comprising object classes (C) assigned to each object (O) in the object feature map (FM) need to be acquired. Subsequently, a training head (TH) configured to determine the authenticity of an object class (C) assigned to the object (O) in the object feature map (FM) from the object feature map (FM) and the object class tag (LO) is added to the neural network (NN). A modified object category tag (LO ') is determined from the set of object categories (C) and the object category tag (LO), wherein at least one object category assignment (C) is modified. The modified object category tags (LO ') are processed together with the object feature map (FM) by a neural network, generating an output, which is compared with an authenticity tag (LT) using a first objective function (OF1), the authenticity tag comprising an indication of the authenticity of each object category (C) in the modified object category tags (LO'). And updating the network parameters according to the comparison result.

Methods and systems for automated double emulsion droplet library generators are described herein. Optimizing the geometry and composition of double emulsion droplets is essential for maximizing their utility in various scientific and industrial applications. The microfluidic method is effective to generate a droplet library, which is useful to optimize droplets. However, monitoring and intervention by a human are necessary to generate droplet library. The disclosure provided herein presents an automated double-droplet library generator that utilizes a fine-tuned object detector and feedback control. The system autonomously produces libraries of single-core double emulsion droplets tailored to user-defined sizes, shell thicknesses, and solute concentrations.

Embodiments can relate to a computer vision object detection system having a processor with object detector (OD) and a light informed shape analysis (LISA) modules. Upon receiving an image scene, the process can cause the OD module to: to identify an object as an object of interest; identify a region of interest for the object of interest; generate a bounding box encompassing the object of interest and the region of interest; and track movement of the object of interest. When the OD detector drops the bounding box for the object of interest, the processor can cause the LISA module to: extract a region of interest for use as an expected region of interest for the object of interest; extract one or more shape contours of the object of interest for use as a representation of the object of interest; and track movement of the object of interest.

The application discloses a kind of dynamic visual monitoring method and device for high-altitude falling object, mainly by high-altitude falling object detector and trajectory predictor two parts consist of, cover two functions of high-altitude falling object event detection based on event camera, high-altitude falling object trajectory prediction, can be realized in certain complex environment for the accurate identification and trajectory tracking of falling object target.Because the event camera itself high dynamic range, low delay etc., compared with the monitoring method based on traditional optical camera, the present application can be applicable to more complex real environment and bring higher monitoring ability.Project results can provide a new solution for the current high-altitude falling object monitoring supervision problem, and bring greater social and economic benefits to society, have important practical significance.

The invention discloses a bidirectional tracking truth value generation method and system based on MLLM and 3D perception, and the method comprises the steps: S1, inputting image data and 3D laser point cloud data which are synchronous in time sequence, processing each frame of point cloud through a 3D target detector, obtaining a 3D detection frame, and then projecting the 3D detection frame to a synchronous image, and obtaining a corresponding 2D BBOX; s2, performing forward tracking and reverse tracking on the 3D detection frame of each frame to generate a forward track and a reverse track, then performing track merging on the forward track and the reverse track, and identifying a suspicious track in the merging process; s3, for the suspicious trajectory, extracting corresponding images and 2D BBOX, constructing textualized and structured semantic descriptions for each group of images and 2D BBOX data, inputting the textualized and structured semantic descriptions to a pre-trained MLLM, outputting semantic association scores and an inference chain by the MLLM, and performing association decision on the suspicious trajectory based on the semantic association scores; and S4, outputting time sequence truth value data including the complete object ID, the 3D detection frame of each frame and the object category.

Systems, devices, and methods related to an object detector and a Deep learning accelerator are described. For example, a computing apparatus has an integrated circuit device with the Deep learning accelerator configured to execute instructions generated by a compiler from a description of an artificial neural network of the object detector. The artificial neural network includes a first cross stage partial network to extract features from an image and a second cross stage partial network to combine the features to identify a region of interest in the image showing an object. The artificial neural network uses a technique of minimum cost assignment in assigning a classification to the object and thus avoids post processing of non-maximum suppression.

This application relates to classifying fingertip motion patterns on-device in real-time as hand gestures. Hand gestures form an intuitive means of interaction in augmented / mixed reality applications. However, accurate hand gesture recognition can be achieved through deep learning models or using expensive sensors. While these deep learning models are robust, they are often computationally expensive and achieving real-time performance remains a challenge. Embodiments of the present disclosure provide systems and methods for classifying fingertip motion patterns as different hand gestures. Red, green, blue images are fed as input to an object detector (MobileNetV2) to output hand candidate bounding boxes, which are then down-scaled to reduce processing time without compromising the quality of image features. The detected hand candidates are then fed to a fingertip regressor, which outputs the spatial locations of the fingertips representing the motion pattern, where the coordinates of the fingertips are fed to a bidirectional long short-term memory network for classifying the motion pattern as different hand gestures.

The invention discloses an embedded chip packaging detection method based on a lightweight neural network. The embedded chip packaging detection method mainly solves the problem that in the prior art, the detection efficiency of advanced packaging image defects of chips with variable scales and a large number of small target defects is low. According to the implementation scheme, the method comprises the steps that an original advanced packaging image data set is collected from an industrial camera of a chip packaging production line, and a training sample and a verification sample are obtained; constructing a chip advanced packaging defect detection network, and training the chip advanced packaging defect detection network; carrying out lightweight and format conversion on the trained network model; constructing a detection program and deploying the model after format conversion on an embedded development board; and running the detection program on the development board to obtain a chip defect detection result. According to the invention, the lightweight defect detection network is designed and subjected to lightweight processing, so that the computing resource overhead is reduced; by improving the general target detector, the defect detection effect is improved; by deploying the network in an embedded computing platform for prediction, efficient detection of the advanced packaging defects of the chip is realized, and the method can be used for a chip packaging production line.

A power transmitter (101) provides wireless power to a power receiver (105). The power transmitter (101) comprises a transmitting coil (103) and a driver (301) arranged to generate a drive signal to cause an output resonant circuit (103, 303) to generate the electromagnetic signal in a power transfer phase. A communicator (307) may communicate with the power receiver (105) using a communication carrier and receive data load modulated on the communication carrier. An object detector (311) may detect an object, and in response, a controller (313) controls a communicator (307) to receive configuration data load-modulated onto a communication carrier. The controller (313) initiates a power transfer phase in response to the configuration data indicating that the power receiver requests a power transfer, and initiates an update phase in response to the configuration data indicating that the update entity contains update data. The update processor (315) controls the communicator (307) to receive update data from the update entity and to perform an update operation using the update data.

The diagnostic device for passenger conveyors aims to provide a diagnostic device for escalators that can improve diagnostic accuracy. The escalator diagnostic device (4) records the signals obtained from the object detector after the escalator is installed as reference data. The diagnostic device (4) records the signals obtained from the object detector after recording the reference data as diagnostic data. During operation in normal operating mode, the diagnostic device (4) determines whether the object equipment has any abnormalities by comparing the diagnostic data and the reference data. The detector group (20) includes at least one of a speed detector, a status detector, a current detector, and a voltage detector.

A method of identifying an object of interest can comprise obtaining first samples of an intensity distribution of one or more object of interest, obtaining second samples of an intensity distribution of confounder objects, transforming the first and second samples into an appropriate first space, performing dimension reduction on the transformed first and second samples, whereby the dimension reduction of the transformed first and second samples generates an object detector, transforming one or more of the digital images into the first space, performing dimension reduction on the transformed digital images, whereby the dimension reduction of the transformed digital images generates one or more reduced images, classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, and identifying one or more objects of interest from the classified pixels.

A device may receive video data and corresponding GPS data and IMU data associated with a vehicle, and may process the video data, with an object detector model, to identify objects and to generate a first feature vector. The device may process the GPS data and the IMU data, with a first CNN model, to generate a second feature vector, and may process the objects and the video data, with a tracking model, to identify positions and classes of the objects and to generate a third feature vector. The device may utilize a second CNN model to generate a matrix of object features based on the first, second, and third feature vectors, and may utilize a spatiotemporal attention selector model or a max pooled model with the matrix of object features to identify a classification of a maneuver of the vehicle. The device may perform actions based on the classification.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of financial science and technology and medical health, and discloses a multi-modality-based visual question and answer method, device, equipment and medium, and the method comprises the steps: obtaining an input image and a question, and inputting the image and the question into a visual language coding model for coding to generate a multi-modality feature; inputting the multi-modal features into an answer reasoning module to generate answers and deep reasoning features corresponding to the answers; inputting the multi-modal features into a basic principle generation module to generate guide features, and inputting the guide features into a large language model to generate a text basic principle; extracting features of the text basic principle to obtain context features, and generating a visual basic principle through an object detector according to the context features, the image and the depth reasoning features; and taking the visual basic principle, the text basic principle and the answers as final interpretable answers. And the transparency and the credibility of the visual question-answering system are effectively improved.

Object detector that features: a receiving unit (220) designed to receive an oscillation signal corresponding to an oscillation of a microphone (11), wherein the microphone is designed to emit a transmitting wave, which is an ultrasonic wave, by being driven to oscillate on the basis of a drive signal while oscillating by receiving a reflected wave generated as a result of the transmitting wave being reflected by an object; a reduction identification unit (245) designed to identify, in a temporal change of an amplitude of the oscillation signal obtained by the acquisition unit, a reduction time (T3) of a first reduction from a value greater than a threshold (X) to the threshold or less after termination of the drive signal; a magnification estimation unit (140) designed to estimate a magnification reverberation time (T0) of the reflected wave based on the reduction time identified by the reduction identification unit, wherein the magnification reverberation time is before the reduction time and after a time of start of the drive signal; and a distance determination unit (150) designed to determine a distance to the object based on the magnification reverberation time estimated by the magnification estimation unit.

The disclosure relates to a device and method for creating a dynamic occupancy grid map. Specifically, a dynamic occupancy grid map creation device comprises an object detector calculating a measurement based on a reception signal received from a radar sensor and detecting an object around a host vehicle, a shape estimator estimating a shape of a target vehicle when the target vehicle is detected, an occupancy probability updater ellipse-fitting the shape of the target vehicle to a point cloud and updating an occupancy probability of a grid for the target vehicle of a dynamic occupancy grid map (DOGM) based on the fitted shape, and a compensator compensating for a position of the host vehicle over time, on the dynamic occupancy grid map.

An image processing system includes an image sensor, an object detector, an artificial intelligence (AI) inference engine and a mode controller. The image sensor is configured to capture an image. The object detector is configured to determine whether an object exists in the image, and utilize a bounding box to frame the object to obtain a region of interest (ROI) image after determining that the object exists in the image. The artificial intelligence (AI) inference engine is configured to recognize the ROI image to obtain a recognition result. The mode controller is configured to compare an ROI size of the ROI image with a lower limit of a ROI size threshold, and switches the image processing system to a crop mode or a binning mode.

Golf trolley comprising wheels (40), a drive unit (10) configured to drive the wheels (40) to move the golf trolley, a camera (21), a control unit (30) configured to autonomously control the golf trolley to perform actions, wherein the control unit (30) comprises an object detector (33) and an action trigger unit (32), wherein the action trigger unit (32) is configured to determine an action trigger determined based on a golf club detected in an image of the camera, wherein control unit (30) is configured to control the golf trolley to perform an action in response to the determined action trigger.