2488 results about "Object detection" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The present invention relates to the technical field of deep learning and computer vision. Disclosed is an unmanned aerial vehicle image-based small object detection method for target areas. The present invention crops images of obvious small objects in certain target areas, and annotates the small objects of different categories to form a raw training and testing dataset, so as to ensure the accuracy of data required in the early stage of the algorithm and further ensure the scientificity of the algorithm; uses the computing capability of an improved YOLOv7 detection model to collect image features of different degrees in the dataset, the improved YOLOv7 detection model using YOLOv7 as a basic model and adding to a neck network an MS-CET module, which is constituted by an improved self-attention mechanism and convolution module SPPCSP, and a BHC-FB module, which is constituted by bidirectional mixed convolution modules NConv and RPConv connected in parallel; and finally fuses different feature layers as a final judgment basis of an unmanned aerial vehicle for small object detection in the target areas, to further check the accuracy of the algorithm and criteria for dataset selection, thereby improving recognition accuracy.

A pest control system is disclosed comprising an optical, computational, and monitoring subsystem, optionally mounted on a mobile platform. The optical system may include a neutralizing laser or multi-wavelength light source, discovery and detail cameras (optionally stereo), a beam-steering mechanism, tunable focus, and optional thermal or depth sensors. The processor, such as a GPU or FPGA, identifies insect or biological targets, adjusts laser focus by depth, and controls beam activation. A monitoring system verifies safety by detecting humans or other non-target entities using environmental and thermal cameras; if detected, laser firing is inhibited. The mobile platform may use wheels, propellers, tracks, or cables, with GPS and data links for remote control. A visible light pre-flash may induce a blink reflex before firing. In some embodiments, a scouting drone transmits target coordinates to the neutralization unit, enabling coordinated, efficient, and safe laser-based pest control.

A perception system may be used to generate bounding boxes for objects in a vehicle scene. The perception system may receive images and feature maps corresponding to the received images. The perception system may correlate object queries from previous time steps with object queries from the current time step.

A system, a method and a processor are for AI-powered generation and delivery of video clips. The processor is configured to: load a first video file of a first video content item, the first video file comprising video frames associated with timestamps; load a first subtitle file of the first video content item, the first subtitle file comprising subtitle text associated with the timestamps; execute a natural language processing (NLP) model with the subtitle text as input, the NLP model including language pre-processing steps for classifying words, names or phrases in the subtitle text and associating initial classifiers with the subtitle text, the NLP model including one or more of a recurrent neural network (RNN), a Bidirectional Encoder Representations from Transformers (BERT) model, or a generative pre-trained transformer (GPT) model for a dialogue analysis comprising processing sequences of dialogue in the subtitle text in view of the initial classifiers to associate one or more portions of the dialogue with one or more first classifiers of first narrative elements; execute an image recognition model with at least some of the video frames as input, the image recognition model including a convolutional neural network (CNN) for an object detection analysis and a facial recognition analysis comprising processing video sequences to associate one or more of the video frames with one or more second classifiers of second narrative elements; generate a narrative map of the first video content item by temporally aligning the first narrative elements with the second narrative elements based on the timestamps associated with the video frames and the first subtitle file; and generate a video clip including at least one segment of the first video content item, the at least one segment including selected video frames associated with at least one of the first or second narrative elements identified from the narrative map and selected for inclusion in the video clip.

A method performed by an apparatus for controlling autonomous driving of a vehicle is introduced. The method may comprise generating, based on a segmentation model processing point cloud data, a first semantic grid map, generating, based on an object detection model, a second semantic grid map, adjusting a probability regarding whether occupancy exists for an element included in each grid of the first semantic grid map and the second semantic grid map, and generating a fused grid map by determining, as a representative label, at least one label corresponding to a highest value among final probabilities of the at least one label, wherein the final probabilities are determined based on whether the at least one label matches the element, outputting, based on the fused grid map, a signal, and controlling, based on the signal, autonomous driving of the vehicle.

The present invention provides an automatic focusing method, an electronic device, and a readable storage medium. The automatic focusing method comprises: using a target detection model to detect a target object in an image frame acquired at an initial focal length by an optical imaging device to be focused, so as to acquire a target object detection result; on the basis of the target object detection result, determining whether a target object is present in the image frame; if yes, determining an actual object distance on the basis of the target object detection result and the initial focal length, and on the basis of the actual object distance and a mapping relationship between the object distance and an optimal imaging focal length, determining the optimal imaging focal length; and if not, using a preset search algorithm to search for a focal length until an image having the highest definition value is found, and using a focal length corresponding to the image having the highest definition value as the optimal imaging focal length. The present invention primarily employs a deep learning-based automatic focusing method, supplemented by an image definition evaluation method. Compared with traditional passive focusing methods, the present invention greatly improves the focusing efficiency. Compared with traditional active focusing methods, the present invention eliminates a need for adding an additional optical ranging component, ensuring that the imaging device has a simple structure and a low cost.

This disclosure describes systems, methods, and devices related to object detection in images. A device may input an image, representing an object, to a manual labeling learner system; identify, using the system, first coordinates of an upper left corner of a bounding box representing the object based on a heatmap indicative of a probability of the first coordinates representing the upper left corner; identify, using the system, second coordinates of a bottom right corner of the bounding box based on the first coordinates and a first distance regression map indicative of coordinate differences between the second coordinates and ground truth coordinates input to the machine learning model as training data; generate, using the system, adjustments to the first coordinates and the second coordinates based on a second regression map; and generate, using the system, the adjusted first and second coordinates, the bounding box.

Embodiments of the present description provide an image object detection method. The method comprises: on the basis of an image to be retrieved, an object description text, and an object retrieval condition, determining, by means of an object detection model, a target position of an object to be retrieved in said image, wherein the object description text is used for describing said object, and the object retrieval condition comprises at least one of a mask image, a pose, and a texture corresponding to said object.

The invention relates to the technical field of underwater static target object detection and processing, in particular to an unmanned cross-domain positioning and acoustic fingerprint processing method and system for an underwater static target. Comprising the following steps: performing wide-area scanning on a task sea area, and scheduling an AUV and unmanned aerial vehicle cluster to a target area after discovering a suspicious target; the unmanned aerial vehicle cluster receives an acoustic signal of the AUV and transmits the acoustic signal to the cooperative resolving center in combination with self-positioning data so as to provide accurate coordinate guidance for the AUV; the AUV sails to a target area, parallel processing map construction and accurate positioning, target identification and dual-mode fingerprint generation are carried out, and a target task package is packaged; the ROV receives and analyzes the task packet, autonomously plans a path and sails to a target area, a sonar is started to collect data and generate real-time feature fingerprints, and the real-time feature fingerprints are matched with fingerprints in the task packet; and after matching succeeds, a specific job task is autonomously executed. According to the invention, a complete automatic solution is provided for scenes such as deep and far sea detection, emergency salvage, pipeline maintenance and the like.

In various examples, systems and methods for uncertainty estimation for object detection in autonomous and semi-autonomous systems and applications are provided. The systems and methods may use data from one or more sensors (e.g., camera(s) and / or LiDAR sensor(s) to generate a representation of features surrounding a machine. A model may be used to generate probabilities of objects being present in the representation of features and uncertainty estimates corresponding to the object presence probabilities. The uncertainty estimates may be used to identify scenes that are significantly different from the training data, detect errors in the bounding shapes for objects, and / or highlight areas where object detections may have been missed. The systems and methods may also be used to auto-label scenes associated with the representation of features, and the auto-labeled scenes may be used for training purposes.

The invention discloses an electric power system equipment image anomaly detection and quality diagnosis method based on a multi-modal visual language model. The method comprises the following steps: constructing a large-scale multi-modal data set comprising an electrical equipment image, an object detection annotation, a pairing question and answer knowledge base and an official supervision document, constructing a basic diagnosis model based on a visual language model, and carrying out instruction tuning; carrying out post-training on the model by adopting group relative strategy optimized reinforcement learning, and generating an interpretable step-by-step diagnostic reasoning chain; in the reasoning process, related knowledge is dynamically retrieved based on a retrieval enhancement generation technology of a graph structure, and the accuracy and compliance of a diagnosis decision are enhanced; and finally, generating a diagnosis report containing the exception type, the root cause and the decision suggestion. Compared with a traditional method, the method solves the three problems of data scarcity, opaque reasoning and knowledge isolation in the field of electric power detection, and has the remarkable advantages that the diagnosis process can be explained, complex multi-step reasoning is supported, and domain knowledge can be dynamically integrated.

The embodiment of the invention provides a vehicle driving track prediction method and system and electronic equipment. The method comprises the steps that vehicle driving information corresponding to a target vehicle is determined; obtaining aerial view feature information according to the vehicle driving information; determining query feature information, semantic segmentation information and a target object detection result according to the vehicle driving information and the aerial view feature information; determining predefined anchor point information, and obtaining first driving track information corresponding to the target vehicle according to the predefined anchor point information and the query feature information; and obtaining more accurate second driving track information corresponding to the target vehicle according to the aerial view feature information, the query feature information, the semantic segmentation information, the target object detection result and the first driving track information. Therefore, based on a strategy from coarse to fine, the relatively rough first driving track information can be converted into the relatively accurate second driving track information, so that a more accurate vehicle driving track is obtained, and the accuracy of the vehicle driving track is improved.

A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.

A computer-readable recording medium having stored therein a fraud detection program causing a computer to execute a process including obtaining a result of object detection by inputting a target image group including a self-checkout-apparatus in an imaging range, into a model trained using a target image and an annotation, and performing fraud detection at the self-checkout-apparatus based on information about an item registered thereto and the result. The target image is identified by calculating statistical information of a position of a detection region of an object in each image in a first group based on positions by inputting the first group into the model, obtaining a position in each image in a second group using the model, and identifying the target image in which a region having an appearance probability equal to or less than a threshold is present, from the second group, based on the statistical information.

Systems and methods for object tracking are described. One or more aspects of the systems and methods include receiving a video depicting an object; generating object tracking information for the object using a student network, wherein the student network is trained in a second training phase based on a teacher network using an object tracking training set and a knowledge distillation loss that is based on an output of the student network and the teacher network, and wherein the teacher network is trained in a first training phase using an object detection training set that is augmented with object tracking supervision data; and transmitting the object tracking information in response to receiving the video.

The invention provides a vehicle throwing object detection and positioning method and system based on multi-channel spatial-temporal feature fusion, and the method comprises the steps: obtaining a continuous time sequence image frame sequence of a road scene, and generating an optical flow image sequence through an optical flow algorithm; outputting a detection frame of each vehicle in each standardized image through a deep neural network target detection model, and generating a region of interest according to the detection frames; a fusion feature vector is generated for each region of interest, a space-time fusion feature sequence is constructed, and a thrown object classification result and a positioning result of each region of interest are generated based on the space-time fusion feature sequence and the multi-branch full-connection network; and based on the classification result and the positioning result of the thrown object, inputting the obtained coordinates of the suspected area of the thrown object into a spherical camera for tracking. According to the method, the thrown object can be efficiently and accurately detected and positioned automatically, the accuracy and real-time performance of thrown object detection are improved, the false alarm rate is reduced, and the precision degree of positioning the position of the thrown object is improved.

The invention discloses a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method based on industrial vision, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting and preprocessing an initial environment image of a current working scene of a mechanical arm, obtaining a standardized working scene image data set, carrying out dynamic object detection, and obtaining an environment perception evaluation report; identifying an unobserved area of the working scene based on the environment perception evaluation report, performing collision path calculation, and generating a visual angle adjustment action instruction; the safety path sequence is issued to a mechanical arm joint and executed, images in front of the mechanical arm are continuously collected during execution, and if an unpredicted sudden obstacle is detected, the four-dimensional risk map is dynamically updated, and path re-planning is conducted; and when the tail end of the mechanical arm successfully completes the task, obstacle avoidance path planning data of the mechanical arm are recorded and stored persistently, and an obstacle avoidance path planning record is generated. According to the method, the real-time problem of path planning of the mechanical arm is solved through construction of the four-dimensional dynamic risk map.

The present application relates to a fine-tuning-based cross-scenario object detection method and system, a device, and a medium. The method comprises: improving a backbone network of a YOLOv8 algorithm by means of Transformer modules, and inputting an image in a first scenario into an optimized YOLOv8 model for training to obtain a first object detection model; embedding a LORA model into each Transformer module in the first object detection model, training the model on the basis of an image in a second scenario, fixing parameters of the first object detection model, and training model parameters of each LORA model; and on the basis of the trained model parameters of each LORA model, fine-tuning a weight parameter generated by each Transformer module in the first object detection model, to obtain a second object detection model applicable to the second scenario. The present application enables object detection models to adapt to different actual application scenarios within a short period of time, thereby improving the accuracy and efficiency of cross-scenario object detection and recognition.

In multimedia object tracking and merging of tracked objects, an object is tracked through frames of multimedia content until a frame appears in which the tracked object is not detected. A first track is designated as one or more consecutive frames in which the tracked object is detected, the first track ending at the first frame. Tracking continues to try to detect the tracked object in a second frame subsequent to the first frame. If the tracked object is not again detected, information about the first track is output. If the tracked object is detected subsequently, a second track of consecutive tracked object detection is designated. The tracked objects in the two tracks are then compared with the aid of trained data models, and a matching score is determined to reflect the degree of match. If the matching score meets or exceeds a first threshold, the compared tracks are merged using the same identifier assigned to both tracks. If the matching score does not exceed a second threshold that is less than the first threshold, the tracks may be discarded as showing no match. If the matching score falls between the first and second thresholds, an indication is output for further analysis of the compared tracked objects.

A system and method for contextual three-dimensional messaging in augmented reality (AR) environments is disclosed. The system receives chat messages with specified real-world destinations and stores them associated with those locations. When a user wearing an AR device enters a destination location, the system detects their presence using techniques like GPS, Wi-Fi positioning, or computer vision. It then generates a 3D visual representation of the message and determines an appropriate spatial position within the physical environment based on environmental analysis and object detection. The 3D message is displayed at the determined position in the AR view. The system can analyze message content to identify topics and match them to detected real-world objects for contextual placement. Users can interact with displayed messages through gestures or voice commands to reply, forward, delete, or reposition messages. This enables immersive, location-aware messaging experiences that seamlessly blend digital content with the physical world.

Systems and techniques are described herein for adapting a pretrained machine learning model. For instance, a process can include encoding a training image into a first feature vector, the training image including a first object located at a first location; generating a second feature vector based on a set of sinusoidal functions using a set of weights; combining the first feature vector with a second feature vector to generate a combined feature vector; processing the combined feature vector using a visual language model to obtain a second location for the first object; and adjusting the set of weights based on a comparison between the first location and the second location.

The invention belongs to the technical field of power system optimization scheduling, and discloses an intelligent port logistics energy scheduling method based on multi-modal digital twinning, and the method specifically comprises the following steps: obtaining collected multi-modal perception data of a port operation scene; processing the multi-modal perception data through an evolutionary reinforcement learning-based target detection algorithm, and obtaining optimized target detection information in combination with a spatial semantic attention mechanism; performing multi-modal fusion on the target detection information and the multi-modal perception data to generate a port global situation semantic vector; constructing a multi-granularity digital twin model based on the semantic vector, and performing simulation prediction; generating a cooperative scheduling strategy of the logistics and energy system according to the simulation result; according to the method, the problems of insufficient multi-modal perception fusion, lack of logistics energy collaborative optimization and insufficient simulation modeling precision in the prior art are effectively solved by combining a target detection algorithm based on evolutionary reinforcement learning with a spatial semantic attention mechanism.

Provided in the present disclosure are a training method for an object detection model for a low-quality image, an object detection method for a low-quality image, and a related device. The training method comprises: acquiring a low-quality sample image for a target region; inputting the low-quality sample image into a first encoder to obtain an initial feature of the low-quality sample image; inputting the initial feature into a transfer convolutional network, and on the basis of a first target feature, training the transfer convolutional network to obtain a target convolutional network, wherein the first target feature is obtained on the basis of the initial feature; on the basis of the target convolutional network, obtaining a second target feature for the low-quality sample image; inputting the second target feature into a first detection head, and training the first detection head to obtain an object detection head, wherein the first detection head is obtained on the basis of the first target feature; and on the basis of the first encoder, the target convolutional network and the object detection head, obtaining an object detection model, wherein the object detection model is used for performing object detection on a low-quality image to be subjected to detection. The present disclosure provides technical support for realizing efficient object detection.

The invention provides a 3D target detection tracking method based on visual image and radar tensor sparse proposal fusion, which comprises the following steps: firstly, carrying out generalization extraction on color texture information of a visual image, and establishing multi-scale semantic high-dimensional features; secondly, extracting multi-scale space high-dimensional features of a radar tensor by using SCAN, respectively mapping a learnable sensing probe to radar and visual high-dimensional feature spaces, and performing generalization sparseness on different modal features by means of multi-head deformable attention to form radar and visual proposal features; sparse proposal fusion of radar and visual proposal features is carried out, and 3D target detection is completed; and finally, carrying out mixed multi-feature cascade matching and batch track management on a detection result, and feeding back generated track time sequence information to a front-end learnable sensing probe to realize an active target detection and tracking integrated circulating progressive network based on time sequence information guidance. According to the scheme of the invention, an integrated active sensing framework of single-frame target detection and time sequence target tracking is established, and the reliability of environment target sensing by a multi-source sensor in automatic driving is enhanced.

This invention relates to the field of object detection technology, specifically to an improved YOLOv5 object detection method suitable for low-light environments. The method includes offline enhancement of the training set of a low-light dataset using an image enhancement algorithm to obtain an enhanced dataset; pairing and mixing the enhanced dataset with the original training set to obtain a mixed dataset; improving the baseline network to obtain an improved network model; training the improved network model using the mixed dataset to obtain an object detection network model; and inputting the image to be detected into the object detection network model for training to obtain the detection result. This invention, through a hybrid enhancement training method, enhances the low-light dataset using a GAN algorithm and then mixes it with the original training set, effectively suppressing the feature destruction problem caused by directly using enhancement algorithms, and solving the problem of low object detection accuracy in low-light environments in existing object detection methods.

Techniques are provided for filtering object detection information provided via V2X communication links. An example method for partially filtering a sensor data sharing message includes receiving the sensor data sharing message from a vehicle, decoding a first portion of the sensor data sharing message to determine an area of interest associated with the vehicle, determine a relative position of the area of interest, and decoding or discarding a second portion of the sensor data sharing message based on the relative position of the area of interest.

According to one or more embodiments of the present disclosure, an ellipse model may be applied to detection results included in ultrasonic sensor data (USS data) to identify one or more objects that may be indicated by the detection results. The identification of the objects may include determining the locations, shapes, and / or classifications of at least portions of the objects. For example, in some embodiments, the ellipse model may be applied to detection arcs indicated in USS data and corresponding to detection of an object using multiple ultrasonic sensors. In some embodiments, the application of the ellipse model may include fitting an ellipse to the detection arcs. The resulting ellipse and / or its corresponding ellipsoidal parameters may indicate one or more properties about the object.

The invention discloses a dynamic scene real-time SLAM system fusing target detection and optical flow. The system comprises an image input module; a target detection module; an adaptive depth estimation module; when the current frame does not receive the mask from the target detection module, the self-adaptive mask compensation tracking module predicts a dynamic area mask of the current frame from the mask of the previous frame based on an optical flow method and a motion model, and when the system judges that a key frame needs to be inserted, the current frame enters the dynamic point filtering module to be processed; if not, directly carrying out local mapping and closed-loop detection; the dynamic point filtering module is used for eliminating dynamic feature points in a dynamic region and retaining static feature points based on antipode constraint and motion consistency analysis of a reverse optical flow; and a pose estimation and mapping module. According to the method, dynamic object interference is effectively identified and eliminated, high positioning precision is ensured to be obtained in a high dynamic scene, the operation speed of the system is greatly improved, and the real-time requirement is met.