1270 results about "Onboard camera" patented technology

The invention relates to the technical field of intelligent driving, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

The invention relates to the field of computer vision, and provides a dynamic map modal semantic segmentation method and system for vehicle-mounted mining video monitoring. A mining area video is captured through a vehicle-mounted camera, video frame features are deeply extracted by using a pre-trained first convolutional neural network, multi-layer features are fused to enhance the representation capability, then a time dimension dynamic map and a space dimension dynamic map are constructed, a dynamic map mode is formed after fusion, an attention mechanism is introduced, and the attention of the mining area is enhanced. And distributing weights according to the similarity of the feature vectors, and focusing the correlation among the key areas. And extracting context information by using a global average pooling layer, and combining with the weighted original feature map to fuse global and dynamic map information. In addition, local context information is also combined to enhance feature understanding. And outputting an accurate semantic segmentation result based on the comprehensively fused feature map, and distributing a label representing an object or a background category to which each pixel belongs for each pixel, thereby realizing deep understanding and efficient analysis of mining area video information.

The invention discloses a traffic operation and maintenance fault intelligent scheduling method and system based on an AI large model, and belongs to the technical field of traffic control. The method comprises the following steps: collecting a vehicle driving track GPS coordinate set, a traffic flow density matrix, a vehicle-mounted camera monitoring image frame sequence and a fault vehicle owner speed anomaly detection result in real time; constructing a traffic operation state analysis model, and outputting real-time traffic operation state characteristics; generating a traffic fault probability distribution curved surface in a future time window; generating a comprehensive fault positioning confidence coefficient matrix; and planning an optimal maintenance resource path according to the pheromone updating rule, and updating the optimal maintenance resource path to the visual scheduling platform in real time. According to the method, space-time diagram convolutional network dynamic modeling is constructed according to multi-source data, so that the limitation of a space blind area of a single data source is broken through, the fault positioning speed is improved, and the problems of incomplete coverage and low positioning speed in the prior art are solved.

The invention discloses an automobile data recorder fatigue driving detection and reminding method based on multi-modal data fusion, and the method comprises the following steps: S1, collecting and preprocessing a face image through a vehicle-mounted camera, and carrying out the face detection and region positioning; s2, dynamically adjusting the size and the position of an image block by adopting a dynamic image blocking technology; s3, performing facial image feature extraction by using a visual converter; s4, acquiring vehicle multi-modal data and fusing the vehicle multi-modal data with the facial features; s5, optimizing a visual converter network structure and training hyper-parameters through a wolf pack optimization algorithm; s6, performing fatigue state analysis based on the optimized visual converter; and S7, when the fatigue state is detected, reminding the driver to have a rest through sound-light alarm. The fatigue driving detection and reminding method of the automobile data recorder is high in precision and high in real-time performance through a deep learning algorithm of multi-modal data fusion and optimization, and the recognition accuracy of fatigue driving and the robustness of the system are effectively improved.

The invention relates to an automatic driving YOLO target detection method and system for coping with light influence, and the method comprises the steps: employing a vehicle-mounted camera to shoot the actual condition images of streets and roads on a plurality of streets, obtaining the vehicle actual condition images of different streets and different roads in different time periods, and forming an image data set; marking the image data set by using a LabelImg tool, and marking a vehicle target in the image, including a bounding box and category information of a vehicle; constructing an automatic driving YOLO target detection network model coping with light influence; and training an automatic driving YOLO target detection network model coping with the light influence by using the obtained image data set, wherein the trained automatic driving YOLO target detection network model coping with the light influence is used for coping with vehicle target detection under the light influence. Accurate vehicle distance adjustment and safe driving decisions can be provided for the automatic driving vehicle, and driving safety and efficiency under various light conditions are ensured.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention provides a driving behavior intelligent identification method and system based on video analysis, and the method comprises the steps: obtaining a driver face video stream and a road environment video stream collected by a vehicle-mounted camera, and reading the driving information recorded by a whole vehicle communication network; recognizing an eyelid closing state, a sight line direction and a head posture in the driver face video stream based on a posture recognition model, and performing fatigue distraction analysis to obtain driver state information; performing motion trail analysis on the road environment video stream and the driving information, and performing driving risk assessment in combination with the driver state information to obtain driving assessment information; and performing early warning construction according to the driving evaluation information, generating early warning prompt information, and synchronously writing the early warning prompt information, the driving evaluation information and the driver state information into a safety data protection memory. The fatigue and distraction states of the driver can be recognized more accurately, and the accuracy of state judgment is improved.

The invention discloses a vehicle-mounted driving assistance method, which relates to the technical field of driving assistance and comprises the following steps: acquiring road image data acquired by a vehicle-mounted camera and vehicle state data acquired by a vehicle-mounted sensor; performing image recognition on the road image data, extracting traffic signs, lane lines and surrounding vehicle position information, and generating road environment data; and judging a current driving risk level according to the road environment data and the vehicle state data, and feeding back early warning information to a driver according to the driving risk level. According to the invention, a comprehensive utilization and real-time feedback mechanism of multi-source data is provided, and the perception and reaction capability of a driver to potential risks is enhanced. Therefore, the system not only can deal with conventional driving situations, but also can deal with complex and dynamically changing traffic situations, so that the driving safety is greatly improved, and the possibility of accidents is reduced.

The invention relates to the technical field of intelligent driving, and discloses a defogging embedded system and method for an intelligent driving vehicle-mounted camera, and the method comprises the steps: obtaining original image data collected by the vehicle-mounted camera, and analyzing the brightness distribution, color shift and texture features in an image; based on the image degradation feature information, performing preliminary enhancement processing on the image by adopting an edge guided filtering and brightness adaptive mapping algorithm to generate an initial enhanced image; carrying out fog distribution estimation on the initial enhanced image, carrying out detail restoration and color compensation in combination with a lightweight image reconstruction strategy, and dynamically improving the image quality through an iterative optimization mode; performing definition evaluation on the optimized image, and measuring the fuzzy degree change trend and the structural similarity; and in combination with the finally output clear image, performing target identification and feature extraction operation, and performing accuracy evaluation on an identification result. The method has the advantage of improving the definition of the intelligent driving image.

The invention provides an ai-driven mud head vehicle illegal behavior cooperative monitoring method and system, and the method comprises the steps: collecting the three-dimensional acceleration and three-dimensional angular velocity data of a mud head vehicle through a vehicle-mounted inertial sensor, and constructing an inertial behavior sequence; establishing an individualized behavior model based on historical normal behavior data, identifying an abnormal behavior period deviating from a normal state, and generating an abnormal score; when the abnormal behavior exists, a vehicle-mounted camera is triggered to collect an environment image, and the legality of the scene where the mud-head vehicle is located is judged through a visual semantic recognition model; and fusing the abnormal score and the scene legality mark, and generating a final violation judgment result of the violation behavior through a joint judgment model. And according to the violation judgment result, triggering a multi-stage linkage response mechanism, including vehicle-mounted real-time feedback, regional monitoring equipment linkage and violation data uploading and evidence storage.

A method, device, equipment and medium for locating water accumulation based on three-dimensional oblique photography of unmanned aerial vehicles. A three-dimensional virtual scene graph is constructed based on three-dimensional terrain data obtained by oblique photography of unmanned aerial vehicles, and a target fitting plane is generated by fitting its point cloud data through a random sample consistency algorithm. The uneven area set is determined according to the height difference between each point cloud and the target fitting plane; the road surface image obtained by a vehicle-mounted camera is input into a water accumulation detection model generated by training an adaptive dynamic channel pruning strategy to obtain a detection frame set, and the strategy is to calculate the square of the channel weight amplitude and normalize it to the sum of the weight squares of all retained channels in the same layer to obtain an adaptive dynamic channel pruning score and prune the channel according to its size; the uneven area set and the detection frame set are weightedly fused to obtain a fused detection area; the fused detection area with a confidence greater than a confidence threshold is taken as a water accumulation area and its coordinates are output, which can improve detection efficiency and reduce costs.

A vehicular camera includes a lens unit, a circuit board, an imaging element, a housing supporting the lens unit and accommodating the circuit board and the imaging element, a shield made of metal that is disposed in an internal space of the housing to surround the circuit board, and a resin member that contains a magnetic material, has a predetermined thermal conductivity, and is disposed between the circuit board and a first bottom surface portion of the shield in a manner of extending over an entire circumference of the hole including a central portion of a second shape of the first bottom surface portion of the shield. The resin member is in contact with a second surface of the circuit board, first to fourth side surface portions of the shield, and first bottom surface portion of the shield.

A vehicular camera monitoring system includes an electronic control unit (ECU) at a vehicle and a support arm movably disposed at a side portion of the vehicle, with the support arm having a base end attached at the side portion of the vehicle and a distal end opposite the base end. A camera is disposed at the distal end of the support arm. The support arm is movable between a stowed position and an extended position. A cover element covers an aperture at the side portion at least when the support arm is in the extended position. The camera, when the support arm is in the extended position, captures image data and provides captured image data to the ECU, which processes the provided image data for (i) display of video images derived from provided image data and / or (ii) detection of an object in the field of view of the camera.

The invention discloses a multi-degraded image restoration method based on semantic guidance. According to the method, for degraded images captured by a vehicle-mounted camera under severe weather conditions, potential features of the images are extracted by adopting a trunk network based on Transform, multi-modal semantic information is extracted in combination with a CLIP visual language model, and semantic guidance is provided for different degradation types through a dynamic text prompt generation mechanism. A self-adaptive feature fusion module is designed, channel attention and space attention mechanisms are combined to realize effective integration of multi-modal features, and a degradation feature extraction and fusion module is introduced to enhance the generalization ability of the model. According to the semantic guidance multi-type image recovery network SGIRN provided by the invention, the global modeling capability of the Transform and the cross-modal representation capability of the CLIP visual language model are combined, so that high-quality recovery of various weather degradation types is realized.

The invention provides an automobile lamp control system and method based on DLP projection, and relates to the technical field of automobile lamp projection control. Comprising a vehicle-mounted camera module, a navigation module, a vehicle-mounted controller, a millimeter-wave radar module and a DLP photographing module, the vehicle-mounted camera module is used for capturing gesture information and sight line information of a driver in real time; an image processing module and a control module are integrated in the vehicle-mounted controller; the image processing module is used for analyzing and recognizing the received gesture information and sight line information of the driver; the millimeter-wave radar module is used for detecting vehicle surrounding environment information; the navigation module is used for sending navigation information to the control module when a vehicle starts a navigation function; the control module is used for carrying out fusion processing on the received information based on a preset algorithm, a rule base and a gesture base, generating a control instruction corresponding to the information and sending the control instruction to the DLP photographing module. The driving safety is improved, the interaction convenience is enhanced, and the information display is optimized.

Various systems and methods are presented regarding utilizing technology onboard a vehicle to mitigate the effects of communication signals being deleteriously affected / lost when operational surroundings cause occlusion / loss of receipt of positioning / location signals at the vehicle. The vehicle can be operating non-autonomously, partially autonomously, or autonomously. To supplement the lost / occluded signals position information of other systems proximate to the vehicle can be obtained / utilized. The other systems can include another vehicle, a mobile device, an internet, and suchlike. Also, onboard devices can be utilized to determine the location, such as an onboard camera system providing information regarding street signs, building facades, and suchlike.

Methods and apparatus for onboard camera calibration are disclosed. A disclosed apparatus for use with an aircraft includes interface circuitry communicatively coupled to a camera of the aircraft, the camera to capture an image, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to generate randomized images from images stored in a data storage carried by the aircraft, determine at least one relationship between an image captured by the camera while the aircraft is in flight and the randomized images, and determine a parameter of the camera based on the at least one relationship to calibrate the camera.

The invention discloses a multi-source fusion real-time traffic perception and emergency early warning system and method based on vehicle and road cloud cooperation, and relates to the technical field of traffic management. The vehicle end system comprises an information acquisition and transmission module, a local decision module, a data receiving module, an emergency response module and a data feedback module, and the information acquisition and transmission module acquires vehicle peripheral information through a vehicle-mounted radar and a vehicle-mounted camera. According to the invention, a vehicle end collects local information such as obstacles around a vehicle and dynamic states of adjacent vehicles through a vehicle-mounted radar and a camera, a road end collects regional information such as road section traffic flow density, road damage and traffic facility states through a roadside camera and a sensor, and a cloud end integrates data at the two ends and carries out time-space synchronization through a multi-source fusion technology. The defect of a single vehicle end or road end sensing blind area is overcome, and complete sensing of the traffic state of the whole road network is achieved.

A vehicular camera includes a lens unit, a circuit board, an imaging element, a plurality of electronic components disposed on first and second surfaces of the circuit board, a circuit board connector connection portion disposed on the second surface of the circuit board, a housing including a second tubular portion that accommodates at least the circuit board and the imaging element, a metallic first planar member covering at least a part of the plurality of electronic components disposed on the second surface of the circuit board and electrically connected to the circuit of the circuit board, a connector including at least one terminal that extends across an inside and an outside of the housing, and a metallic tubular member that surrounds at least a part of the at least one terminal of the connector around the direction along the optical axis and has a tubular shape.

The present disclosure provides a method and apparatus for generating an aerial view of a vehicle, a storage medium, an electronic device, and a vehicle. The method comprises: obtaining a road scenario image set of a plurality of directions acquired by a vehicle-mounted camera of a target vehicle; preprocessing the road scenario image set into a target image set of a target size, and obtaining camera parameters of the vehicle-mounted camera; using a convolutional network to extract multi-scale image features of the target image set, and using the central point of the target vehicle as an original point to create a three-dimensional coordinate grid; and using the multi-scale image features, the camera parameters, and the three-dimensional coordinate grid to generate an environment aerial view of the target vehicle.

The invention discloses a driving environment identification method and system based on a vehicle-mounted camera, and the method comprises the steps: obtaining detection image data, and carrying out the preprocessing of the detection image data, and obtaining the preprocessed image data; inputting the preprocessed image data into a preset driving environment recognition model, and determining driving environment recognition data, a recognition target and a risk level of the recognition target; dividing the detection area into a plurality of sub-detection areas, adjusting first attention scores of the sub-detection areas based on an attention mechanism, and analyzing next detection image data according to the first attention scores; comparing the detection image data of the adjacent acquisition time, and determining a newly added target and a disappearing target; and extracting local image features of the disappearing target, adjusting a second attention score of the sub-detection region, and analyzing next detection image data according to the second attention score. According to the invention, by adjusting the attention score of each sub-recognition area, the recognition efficiency of the recognition target in the driving environment is improved.

The invention relates to the technical field of electric connection, in particular to a vehicle-mounted camera connector assembly which comprises a shell connector and a plate end connector, the shell connector comprises a shell, a power connection assembly and a fixing plug, the power connection assembly is arranged in the shell, and the fixing plug is used for fixing the power connection assembly in the shell. The power connection assembly is provided with a first power connection terminal, a first plugging end and a second plugging end. The two ends of the first power connection terminal extend to the first plugging end and the second plugging end respectively. The board end connector comprises a base, a floating seat, a fixing piece, a floating connecting piece and a second power connection terminal, the base and the floating seat are both independently formed by insulating materials, and the fixing piece and the floating connecting piece are both independently formed by conductive metal pieces. Stable and reliable electric connection, good floating compensation, buffer protection and efficient and convenient assembly and maintenance are achieved, and the high-quality signal transmission requirement of the vehicle-mounted camera in a complex vehicle environment can be met.

The invention relates to the field of automobile electronic testing technology and equipment, and discloses a vehicle-mounted camera imaging quality testing method and device suitable for a rain and fog scene, and the method comprises the steps: installing a to-be-tested camera module in a rain and fog environment cabin, setting the spectrum and illumination of a top natural illumination simulation system, and simulating the illumination at different time periods; selecting a test target plate in the darkroom; starting an automatic centering module to align the optical axis with the standard plate reference; rain and fog parameters are set through the environment control host, the rain and fog supply system is started to simulate different levels of rain and fog environments, and the raindrop particle size, concentration, dripping speed, fog visibility and fogdrop particle size are dynamically regulated and controlled; shooting a target plate graph card, extracting characteristic parameters by an image analysis module, performing quantitative analysis, and generating an evaluation report, so as to solve the technical problem of singleness of rain and fog scene environment simulation in the prior art.

The invention belongs to the technical field of image processing, and particularly discloses a vehicle-mounted camera image denoising and enhancing method and system, an image is decomposed into sub-band images with different frequencies through multi-scale decomposition, so that noise and details are processed on different scales, aiming at the characteristics of different sub-bands, a self-adaptive algorithm is adopted to estimate noise intensity, and the noise intensity is improved. According to the method, a high-frequency sub-band image is estimated, adaptive denoising processing is carried out according to the estimated noise intensity, the denoising effect is ensured, meanwhile, blurring of edges and textures is reduced, the processing quality is improved, detail enhancement operation is carried out on the denoised high-frequency sub-band image, a deep learning model structure and parameters are optimized in the training process, and the robustness of the high-frequency sub-band image is improved. The method can effectively improve the image quality under limited computing resources, the design of the deep learning model reduces the calculation amount, the powerful feature learning capability of the deep learning model can capture the image features which are difficult to process by a traditional method, and the denoising and enhancement effects are further improved.

The present invention provides a vehicle-mounted assisted driving instrument, which relates to the field of vehicle-mounted assisted driving technology. The vehicle-mounted assisted driving instrument includes a main body, a bracket and a rotating part; the main body includes a controller and a display screen connected to a vehicle-mounted camera, the display screen is arranged on one side of the main body, and the controller controls the display screen to display the vehicle-mounted image according to the vehicle-mounted image formed by the image data collected by the vehicle-mounted camera; the bracket is rotatably connected to the main body through a rotating part, and the bracket rotates relative to the main body to form a bracket state and a storage state, and the main body is connected to a smart terminal device for communication. By arranging the bracket on the main body, the main body and the bracket are integrated together, so that the whole machine has both the auxiliary driving function and the bracket function, and through the communication connection between the main body and the smart terminal device, data can be transmitted between the two, and the vehicle-mounted image is expanded to a multi-screen display using the smart terminal device.

The invention provides a low obstacle detection method and device based on multi-sensor fusion and a medium, and relates to the technical field of automatic driving. The method comprises the following steps: acquiring a road image acquired by a vehicle-mounted camera, and performing low obstacle detection on the road image by using a preset target detection model to obtain identification information and a first position of a first low obstacle; acquiring radar data acquired by the millimeter wave radar, and determining a second position of a second low obstacle according to the radar data; performing target fusion on the identification information and the first position of the first low obstacle and the second position of the second low obstacle to obtain candidate obstacle information; and obtaining target point cloud data collected by the laser radar, and determining the target position and the target size of the low obstacle according to the candidate obstacle information and the target point cloud data. According to the method, the detection precision of the position and the size of the low obstacle is improved, the detection accuracy is improved, and false detection is avoided.

The invention relates to a road crack detection method and system based on a zero-sample large language model, and relates to the technical field of computer vision and artificial intelligence. The method comprises the steps that a road surface image is collected through an unmanned aerial vehicle or a vehicle-mounted camera, after denoising and contrast adjustment preprocessing, visual features are extracted through a multi-modal model and converted into text description, and an image-text embedding vector is generated; semantic reasoning is performed on the embedded vector based on a large language model, zero sample classification is realized through similarity matching of predefined text tags, and crack types and severity are identified; and generating a restoration suggestion in combination with the road environment and the traffic load data, and outputting a detection result and a user feedback optimization model through a visual interface. The method can realize high-precision crack detection without labeling data, has the advantages of strong generalization, multi-modal fusion, intelligent decision support and the like, and can be widely applied to health monitoring and maintenance of infrastructures such as roads, bridges and the like.

The invention relates to an automobile windshield shelter removing method and system and an automobile, and belongs to the technical field of automobile active safety. The method comprises the steps that the surface of the windshield is monitored through a vehicle-mounted camera; when a shielding object is identified, calculating a view shielding coefficient based on the area, the position, the light transmittance and the type of the shielding object, and determining an initial influence level according to the view shielding coefficient; corresponding cleaning strategies are executed according to the influence levels, windscreen wiper cleaning is started at the low level, pneumatic suction generated by vehicle intersection is used for cleaning at the middle level, and negative-pressure airflow cleaning is induced through dynamic opening of a skylight at the high level; if the shielding is not removed, the influence level is increased step by step, and circulation execution is carried out until the shielding is removed or automatic parking is triggered. According to the method, grading, closed-loop and non-contact intelligent removal of different types of sheltering objects is achieved, and invalid or even harmful operation of a traditional windscreen wiper in a dry sheltering scene is effectively avoided.

The invention relates to the technical field of image processing, and provides an excavator operation safety monitoring method based on visual assistance and a laser radar, which comprises the following steps: acquiring continuous frames of monitoring images in the excavator operation process through a vehicle-mounted camera, and acquiring three-dimensional point cloud data at a plurality of moments through the laser radar; obtaining a target movement degree of each region, screening to obtain a plurality of moving targets and obtaining a movement sequence of the moving targets; obtaining the target influence degree of each moving target at each moment; the comprehensive influence degree of the moving target is obtained; screening an interference target based on the comprehensive influence degree, and obtaining an interference sequence of the interference target in combination with the moving sequence corresponding to each vehicle-mounted camera; and according to the interference target and the interference sequence thereof, collision early warning is carried out through an audible and visual alarm, and safety monitoring of excavator operation is achieved. The invention aims to solve the problem that the blind area and collision detection accuracy of a fusion result can be interfered by respective misrecognition problems of computer vision and laser radar.

An optical axis position adjuster according to the present disclosure includes a target board, a leg portion that supports the target board, and an installation auxiliary jig that connects the leg portion and the vehicle. The leg portions can be installed horizontally with respect to the ground. The installation auxiliary jig is connected to a connecting portion provided in the leg portion and is connected to an emblem provided in the vehicle. The connecting portion is disposed at the center of the leg portion, and the emblem is disposed at the center of the vehicle in the vehicle width direction. Therefore, it is possible to connect the vehicle and the leg portions such that the center of the target board and the center of the vehicle in the vehicle width direction coincide with each other in a plan view.