955 results about "Blind spot" patented technology

The invention relates to the field of rail detection, in particular to a rail transit vehicle obstacle detection method based on multi-modal data fusion, which comprises an environmental perception classification step, a credibility calculation and mode decision step, a detection data acquisition step and an obstacle detection step. A full-scene coverage detection system is constructed by integrating a visual image sensor, a millimeter-wave radar sensor and an ultrasonic radar sensor and utilizing complementary advantages of the three sensors in different environments. When a certain sensor is interfered by the environment, other sensors can still provide effective data support, a detection blind area caused by failure of a single mode is avoided, and the robustness and the detection accuracy of obstacle detection are improved.

The invention discloses a variation test method and device based on an intelligent automation script. The method comprises the following steps: extracting a statement execution sequence and a branch dependency relationship in an original source code by using a context-aware syntax tree, and generating a control flow diagram and a data flow diagram; performing code semantic analysis on the multi-dimensional code features by using a large language model, identifying potential defect types and variation rules, predicting the test efficiency of variants in combination with a deep Q learning model, and generating to-be-processed variants; performing grammar check and equivalence analysis on the to-be-processed variants to obtain effective variants, and testing the effective variants; variation test indexes are calculated, code weak points are positioned, and test blind areas are identified according to survival variants, so that a visual analysis report is generated; and iteratively generating a new test case according to the current survival variant and the visual analysis report, and testing the current survival variant by using the new test case. According to the invention, the variation test efficiency can be improved.

The invention discloses a PCB mainboard defect detection method and system based on multi-sensor fusion, and the method comprises the steps: synchronously collecting multi-modal data through optical, ray and thermal imaging sensors, and achieving the feature fusion through a multi-branch feature extraction network in combination with a cross-modal attention mechanism (a mutual information algorithm dynamically distributes weights). Defect detection is completed through a feature pyramid network and sub-pixel positioning, and finally sensor parameters, models and production processes are adjusted in a closed-loop mode based on detection results. The problems of detection blind areas of a single sensor, insufficient fixed weight fusion precision, missing detection of small defects and the like are solved, all-directional detection from the surface to the inner layer and from the form to thermal anomaly is realized, the detection precision is improved, the defect occurrence rate is reduced through process linkage, and the method is suitable for a high-density PCB mainboard.

The invention provides a multi-camera linkage tracking control method and system, and relates to the field of cameras, and the method comprises the steps: determining the prediction probability that a corresponding target object enters a visual blind area of each camera based on the predicted future movement track of each target object; selecting the visual blind area of which the prediction probability exceeds a preset probability threshold value, marking the selected visual blind area as a target visual blind area, determining all cameras of which the view field overlapping area contains the target visual blind area, and constructing a candidate camera set; when at least two cameras in the candidate camera set lock the same shooting area at the same time, selecting the camera with the highest shooting priority as a target camera; and controlling the target camera to adjust the shooting view angle. According to the method, the camera with the highest priority is selected, the subjectivity of randomly distributing the cameras is eliminated, shooting conflicts caused by the fact that the multiple cameras scramble for the same target object in the shooting area at the same time are avoided, it is kept that the spare cameras can track other target objects, and seamless relay of tracking of the multiple cameras is guaranteed.

The invention is suitable for the technical field of 3D scanning, and provides a figure model 3D scanning and dynamic attitude capturing method and system, which dynamically triggers 3D scanning by monitoring joint movement intensity in real time, continuously collects continuous movement tracks by using an inertial sensor in a non-scanning period, and reversely deduces space-time parameters of a shielded part through multi-light-source shadow boundary evolution. And constructing a spatio-temporal joint evaluation function to adaptively adjust the scanning frequency and the sampling rate, and finally performing spatio-temporal alignment and confidence weighted fusion on the discrete point cloud, the continuous trajectory and the reverse reckoning data to generate a dynamic biomechanical model. The scheme breaks through the bottleneck of resource waste and blind area shielding of traditional fixed frequency scanning, realizes space-time complementation and precision optimization of motion monitoring, and is suitable for dynamic scenes such as movie and television animation, medical rehabilitation, physical training and the like. According to the system, through multi-modal data fusion and self-adaptive resource scheduling, the data integrity, the real-time performance and the biomechanical analysis capability are remarkably improved, and an efficient solution is provided for human motion digitization.

The invention discloses an assembly type refrigerating machine room energy-saving monitoring system based on data analysis, and relates to the technical field of energy-saving control, and the system comprises an interval module which is used for dividing the space of a machine room into a plurality of monitoring intervals, and is bound with a structural unit; the excitation module is used for applying power disturbance to the structural unit to obtain structural excitation data; the identification module is used for generating a heat influence coefficient matrix according to the excitation data and the temperature change of the adjacent monitoring interval; and the calculation module is used for calculating a blind area temperature and outputting an estimated temperature and a covariance based on the heat influence coefficient matrix, the structure energy consumption data and the adjacent interval temperature. According to the invention, the space of the machine room is subjected to three-dimensional grid division through the interval module, and each monitoring interval is bound with the function nodes in the structure of the machine room, so that the temperature monitoring grid with the corresponding relation between the space and the structure is formed, the space expression precision of monitoring data is improved, and the monitoring efficiency is improved. And a structural support is provided for positioning and temperature compensation of a blind area position.

The invention discloses a blind area monitoring and lane changing auxiliary intelligent safety detection system. The system comprises a data acquisition subsystem, a risk calculation subsystem and a lane changing early warning subsystem. The data acquisition subsystem acquires speeds and collision distances of parallel vehicles in dead zones right behind and on two sides of a vehicle through millimeter-wave radars, the camera module acquires front image information, the data preprocessing module fuses multi-source data, and the environment sensing module adjusts a collision threshold according to illumination, rainfall and the like. And the risk calculation subsystem calculates a collision risk value by using a dynamic threat integral and a space threat potential field model, predicts a lane changing trajectory through an LSTM neural network and optimizes risk pre-judgment in combination with a driver intention. And the lane changing early warning subsystem triggers early warning in a grading manner through a multi-modal output unit based on the collision risk value and the lane changing conflict probability. According to the system, through multi-sensor fusion and an intelligent algorithm, blind area monitoring accuracy, environmental adaptability and driver response efficiency are improved, and lane changing safety is guaranteed.

The invention relates to the technical field of semiconductor manufacturing equipment detection, and discloses a wafer boat defect monitoring method and system based on image process.The wafer boat defect monitoring method based on image processing comprises the steps that a miniature autonomous movement detection robot is constructed, acquiring a diffraction pattern, a multi-focal-plane image sequence, a multispectral image and other multi-modal data on the surface of the wafer boat; image processing is carried out by using a phase recovery algorithm, a focal plane stacking algorithm and a multispectral information fusion technology; planning an optimal observation path based on information entropy calculation and observation value evaluation; performing quantitative characterization and risk assessment on the defects; according to the invention, the resolution limit of traditional optical detection is broken through, the detection capability of nanoscale defects is realized, the detection blind area is eliminated, the three-dimensional form and multi-dimensional feature information of the defects are obtained, and a more accurate technical means is provided for wafer boat quality control.

The invention discloses an artificial intelligence denoising method for a speckle shearing interference image based on self-supervised learning. The method is a self-supervised image denoising method based on blind spot learning. According to the method, an improved self-supervised network architecture is adopted, pixel points of an image are input through a random mask and are replaced by neighborhood pixel values, and a blind spot training sample is constructed. The network only learns and predicts an original noise value of a masked pixel point in a training process, and does not depend on a clean image as a supervision signal, so that real self-supervised learning is realized. The de-noising method comprises the steps of constructing a data set meeting training requirements, performing de-noising model training by using a blind spot learning method and a self-supervised network, performing de-noising calculation on a speckle interferogram through a fully trained model, and finally obtaining a de-noised interference image. The method has the characteristics of simplicity and convenience in calculation and high processing speed, and can meet the requirement of large-data-volume image processing.

The invention discloses a tumor image segmentation method and system based on multi-modal image fusion, and the method comprises the steps: firstly extracting the dimension features of CT, MRI and PET, expanding the dimension features into a four-dimensional model, and achieving the initial segmentation of a feature region; performing registration fusion, group collaborative optimization and blind spot compensation to obtain a fused image with high consistency and complete boundary; then texture morphological features are extracted, and suspicious types are intelligently discriminated; dynamically evaluating the reliability of each modal and weighting to generate initial segmentation; and finally, carrying out inclination angle correction three-dimensional reconstruction, carrying out high-sensitivity detection on an invisible boundary, and outputting an accurate target segmentation result. According to the method, the precision and reliability of tumor suspicious region segmentation are remarkably improved, and efficient technical support is provided for subsequent accurate diagnosis and treatment planning of doctors.

The invention discloses a visual guidance positioning system and method in the machine tool flexible shaft assembling process, and relates to the technical field of flexible shaft assembling, a visual system obtains three-dimensional pose data of the tail end of a flexible shaft and a target connector, a six-dimensional force sensor is combined to monitor contact force / torque in real time, the limitation of a single sensor is broken through, and the blind area of the single sensor is solved; visual and force sense coordinate systems are aligned in real time, and coordinate drifting caused by movement of the mechanical arm is eliminated; generating an approximation path based on the pose difference, introducing a compensation amount formula, inhibiting oscillation in combination with an attenuation coefficient, and improving the path smoothness; when the contact force exceeds the dynamic threshold value or the pose difference converges, path correction is automatically triggered, overload damage is avoided, and the assembly damage rate is effectively reduced. The method breaks through the limitation of traditional static path planning, has high precision, high reliability and complex working condition adaptability, and is especially suitable for precise instrument and heavy load machinery assembly scenes.

The invention discloses an intelligent and safe hoisting monitoring method for a large-tonnage I-beam, relates to the field of construction safety, is based on a monitoring system consisting of a sensing layer, a transmission layer, a data layer, a decision layer and an execution layer, and comprises the following steps: deploying the sensing layer, constructing the transmission layer, configuring the data layer, constructing the decision layer and responding to the execution layer. Structural stress is monitored through a high-precision strain gauge and a distributed optical fiber sensor, the posture is monitored through an MEMS tilt angle sensor and a six-axis gyroscope, the state of the lifting appliance is monitored through a tension sensor and a visual recognition sensor, the environment is monitored through an ultrasonic anemograph and a laser radar, and multi-dimensional data collection is conducted through a displacement sensor monitoring device. Full-scene monitoring of the state of the I-shaped beam, the performance of a lifting appliance, the working environment and crane equipment is achieved, potential risks are captured without dead corners, the accuracy and reliability of data under complex working conditions are ensured, the problems that traditional monitoring data are prone to being interfered by the environment and insufficient in precision are solved, and the safety coefficient is increased.

The embodiment of the invention provides a cross-lens personnel retrieval method and system based on cloud edge collaboration, electronic equipment and a storage medium, and belongs to the technical field of image processing. According to the scheme, a retrieval task issued by a cloud is obtained, wherein the retrieval task comprises a target image and a retrieval area; acquiring a video stream of the camera according to the retrieval area to obtain a monitoring image; performing personnel detection processing on the monitoring image to obtain a to-be-retrieved image; performing personnel retrieval processing according to the target image and the to-be-retrieved image to obtain a retrieval result; according to the method and the device, the target can be repositioned after the target is lost in the view blind area, the calculation performance requirement is reduced, and the accuracy and the real-time performance of personnel tracking are improved.

The invention relates to the technical field of perception fusion, and discloses a tug fusion enhanced perception method based on target tracking and obstacle recognition, which comprises the following steps: processing an AIS, an optical fiber compass, a Beidou RTK, a laser radar, a millimeter wave radar and a panoramic camera on a tug to establish a ship coordinate system; and collecting message data of the AIS and combining with the MMSI information to screen the target ship to obtain the initial position of the target ship. And predicting the state sequence of the target ship to obtain the position, speed and heading information of the target ship. And carrying out target identification on the image, outputting identification results of semantic levels of the ship, the buoy and the quay wall, and carrying out space projection alignment with radar data. And fusing the processed results to obtain a fusion result, and performing early warning according to the fusion result. According to the method, the target ship relative pose resolving precision and the obstacle recognition reliability are improved, the sensing link time delay is shortened, and the driver blind area risk is reduced.

The invention belongs to the technical field of image processing, and particularly relates to a GIS early warning method based on cleaning result analysis of a cleaning robot, which comprises the following steps: acquiring original image data acquired by a camera of the cleaning robot; analyzing view angle inclination influence according to the original image data, determining a feature point offset condition, and generating distortion distribution information; performing correction processing on the original image data according to the distortion distribution information to generate corrected first image data; edge features are extracted for the first image data, and contour information related to clean area division is obtained; dividing a cleaning area according to the contour information, and determining cleaning state distribution information of each area block; and generating a GIS blind area distribution map according to the cleaning state distribution information. According to the method, the problems of image distortion, inaccurate region division and the like in a visual system of the cleaning robot are effectively solved, the cleaning efficiency and quality are improved, and a reliable basis is provided for subsequent cleaning path planning.

The invention discloses an automobile driving state blind area monitoring method and a rearview mirror, and relates to the technical field of blind area monitoring. Comprising the following steps of information collection, specifically, sensors and cameras are installed on wheels of an automobile, a steering wheel steering column, an automobile chassis and a steering system respectively, driving state data of the automobile speed, the steering wheel angle, the automobile acceleration and the steering angle and image data around the automobile are collected in real time, and real-time automobile information data are obtained; according to the system, driving state data such as the vehicle speed, the steering wheel angle, the vehicle acceleration and the steering angle are combined, the blind area around the vehicle is monitored in real time through the sensor and the camera, and when the vehicle steers, the blind area monitoring range is adjusted in real time to adapt to the driving direction change; the rearview mirror can display blind area images in real time, the blind area alertness of a driver can be greatly improved, and the traffic accident risk caused by the blind area is effectively reduced.

The invention relates to the technical field of vehicle-road cooperation communication, in particular to an automatic driving blind area compensation method based on vehicle-road cooperation, which comprises the following steps: acquiring coordinate speeds and blind area boundary points uploaded by a vehicle and an RSU, screening boundaries to construct a closed area, calculating an overlapping ratio, extracting track segments, clustering and screening overlapped segments to generate a track set, and compensating the track set. And calculating the overlap ratio and the blind area exposure value, combining with speed normalization sorting to generate a risk avoiding priority, calculating a trajectory intersection difference and a density judgment direction, and correcting a blind area structure to generate a path pre-scheduling instruction set. According to the method, a closed region is constructed through Euclidean distance screening, an overlapping proportion is calculated in combination with a blind area, a trajectory overlapping section is extracted to analyze vector and distance change, a blind area exposure value and a function level are fused to evaluate threats, a trajectory density sequence is constructed to discriminate a risk direction, and a blind area structure is corrected to generate a pre-scheduling instruction. And the path avoidance adaptability and the cooperation efficiency are improved.

The invention relates to the technical field of image processing, and discloses an optical image noise reduction method and system of a composite imaging assembly. The method comprises the following steps: carrying out geometric correction on visible light, infrared and laser sensor data through a multi-sensor time synchronization mechanism to obtain a noise coupling matrix; generating annular and checkerboard type mask modes based on the noise coupling matrix; performing cross-band feature interaction through the conditional mask convolution block to obtain a multi-scale dense connection feature map; a double-blind-spot self-supervised learning algorithm is adopted to optimize cross-band coordination loss, and a noise reduction prediction result is obtained; and finally, edge protection processing is carried out through a block-based Wiener filter, and a final noise reduction image is obtained. The problem of noise reduction failure caused by multi-sensor noise coupling and pixel strong correlation in the composite imaging assembly is solved, and the imaging quality and target recognition precision of the composite imaging assembly in a complex environment are improved.

The invention discloses an efficient filling method for an underground small goaf group, and the method comprises the steps: constructing a three-dimensional model through laser radar scanning, and marking a communication relation and the stability of a top plate; dividing the goaf group into a main filling area and a secondary filling area, and designing a fractal topological structure drainage channel to form a gravity flow network; preparing filling slurry taking a cementing material, tailings and the like as main materials, and controlling parameters such as slump, initial setting time and the like; then, stepped pressure self-adaptive filling is adopted, slurry is injected in two stages, and the pressure and the flow speed are controlled; according to the method, the problems that traditional filling blind areas are large in number, material waste is serious and efficiency is low are solved, low-pump-pressure global coverage is achieved, the roof contact rate is remarkably increased, safe and efficient mining of mines is guaranteed, the comprehensive cost is reduced, and the method is suitable for underground small goaf group filling.

The invention discloses a wafer carrier size laser measurement device, and relates to the technical field of laser measurement, the wafer carrier size laser measurement device comprises a measurement platform mechanism, the measurement platform mechanism comprises a mounting table, the front surface of the mounting table is provided with a control terminal, and the top of the inner cavity of the mounting table is fixedly connected with a mounting rack; and the surface of the mounting frame is fixedly connected with a first motor. According to the invention, through the arrangement of the horizontally distributed self-adaptive nodes, flexible deployment is realized by utilizing a magnetic attraction structure, and the electric telescopic rod and the silica gel contact head are matched, so that complex curved surfaces such as a three-dimensional buckle and a deep groove of a carrier can be attached in a self-adaptive manner; a vertical plane and a concave structure which are difficult to detect by a traditional device can be subjected to multi-angle scanning, a measurement blind area is thoroughly eliminated, the coverage rate of three-dimensional size detection is remarkably increased, meanwhile, a temperature and vibration compensation model is built in an intelligent compensation algorithm module integrated by a control terminal, and the influence of environmental factors on a measurement result is corrected in real time.

The invention discloses an adhesive tape bonding state evaluation method and system, and relates to the technical field of intelligent evaluation, and the method comprises the following steps: screening a key evaluation part, executing first operation after first judgment, carrying out first detection and second detection on a to-be-evaluated adhesive tape, obtaining to-be-analyzed data according to detection parameters after first regulation and control, and carrying out second detection according to the to-be-analyzed data. Carrying out conjoint analysis to obtain a debonding severity degree and a debonding position, and constructing a first mapping relation; high failure risk areas are screened through historical data, the detection efficiency is improved, detection parameters are fed back and adjusted in real time according to the first detection result, the omission ratio is remarkably reduced, image analysis and physical signals of second detection are deeply fused, blind areas of a single detection means are made up, an iterative failure mode library is formed, and the detection efficiency is improved. The parameter self-adaptive design can be compatible with different adhesive tape materials and bonding base materials, and recalibration for each scene is not needed.

A fire scene object infrared contour detection device disclosed by the present invention comprises a top seat and a transparent protective cover, the transparent protective cover is arranged at the bottom of the top seat, the transparent protective cover is internally provided with a detection assembly, the detection assembly comprises a supporting rod, the supporting rod is fixedly connected with the top seat, and the supporting rod is fixedly connected with the top seat. A stand column is fixedly connected to the middle of the supporting rod, a fixing base is fixedly connected to the bottom of the supporting rod, and a first motor is installed on the top of the fixing base. The problem that dust is attached to the outer side of the transparent protective cover to block sight is solved through the outer wall cleaning ring, detection blind spots are avoided, the inner wall cleaning ring is beneficial to preventing the phenomena that the inner wall fogs, the sight is blocked and the detection effect is affected, the transmission cylinder drives the lifting ring on the outer side to ascend and descend, the angle of the adjustable detection head can be adjusted, and the detection efficiency is improved. The detection range can be adjusted according to needs, and the multiple adjustable detection heads are arranged, so that all-directional detection is facilitated.

The invention discloses a rotor unmanned aerial vehicle hazardous substance identification management method and system for complex terrains, and belongs to the technical field of unmanned aerial vehicle monitoring. The system comprises a three-dimensional model generation module, a hovering point acquisition module, a data acquisition module, a hazardous substance identification module, a hazard grade sorting module and a data management module. According to the invention, a three-dimensional terrain model is generated in real time through a terrain complexity quantification algorithm, so that a hovering detection point is dynamically selected on an optimal cruise path through a preset path planning algorithm; visible light, infrared and acoustic multi-mode data are synchronously collected at a hovering point, and feature vectors of hazardous substances are extracted through YOLOv7 network fusion; according to the method, the detection coverage area is expanded under the complex terrain, the identification confidence is improved, the emergency response time is shortened, and the problems that the detection blind area is expanded, the identification confidence is reduced and the response time is prolonged under the complex terrain are solved.

The invention discloses a method and a device for observing an internal flow field of a vortex spinning transparent nozzle based on a particle image velocity measurement system. The geometric similarity error is less than or equal to 0.5% and the light transmittance is greater than or equal to 92%. According to the device, a transparent nozzle is integrated in a vortex spinning system, and the device is composed of a laser light source module, a high-speed image acquisition module and a tracer particle injection module. According to the method, a PIV (particle image velocimetry) technology is utilized, hollow glass bead tracer particles are uniformly put into air flow, light illumination of a double-pulse laser sheet and synchronous image acquisition of a high-speed camera are combined, and non-contact dynamic observation of flow fields of a vortex cavity, an air inlet channel and a fiber outlet area in the nozzle is realized. PIV algorithm processing is carried out on the image sequence, and high-temporal-spatial-resolution flow field parameters such as a velocity vector field, turbulent flow kinetic energy and pressure distribution can be obtained. The problems of disturbance defects and observation blind areas of traditional intrusive measurement are solved, and accurate experimental data support is provided for vortex spinning nozzle structure optimization and flow field regulation and control.

The invention discloses a video processing method and device, equipment and a medium. The method comprises the following steps: acquiring a plurality of initial videos collected by a plurality of cameras; performing picture feature extraction based on the plurality of initial videos to obtain a plurality of static features and a plurality of dynamic features; performing feature fusion on the plurality of static features and the plurality of dynamic features to obtain an interested image, the interested image being used for representing that the initial videos collected by the at least two cameras comprise images of the same area; and based on the interested image, performing video stitching on the plurality of initial videos to obtain a panoramic video. According to the embodiment of the invention, a panoramic image of a blind area can be provided for a vehicle owner in real time, the possibility of an accident is reduced, and a clear video image can be conveniently provided after the accident occurs.

The invention belongs to the technical field of geophysical exploration transient electromagnetic detection, particularly relates to an ultra-wide coal face roof electrical tomography system and method, and is suitable for high-precision detection of a hidden geological structure of a working face roof with the width exceeding 300 meters and the depth within 100 meters in an underground coal mine. A transient electromagnetic perspective detection technology is adopted, and full-coverage imaging of an electrical structure of a working face roof is realized through alternate arrangement of transmitting-receiving roadways and dynamic data fusion; a time-space mapping relation is established in combination with a smoke ring effect diffusion model, and omnibearing imaging of the top plate is achieved; aiming at the refined detection of the hidden geologic structure of the top plate of the ultra-wide coal face (the width is greater than or equal to 300 meters), bidirectional detection and grid subdivision are adopted, and a single-side detection blind area is eliminated through roadway transposition of a transmitting end and a receiving end, so that grid full-coverage imaging in a detection range is realized.

The invention relates to an oral cavity scanner and an oral cavity scanning method, and belongs to the technical field of medical instruments, the oral cavity scanner comprises a host, a handheld scanning gun and a data processing terminal, a main control circuit and a power module are arranged in the host, the handheld scanning gun is connected with the host, and a scanning window is arranged at the front end of the handheld scanning gun. The method comprises the following steps: acquiring three-dimensional shape data of oral tissues through a main white light scanning module; the auxiliary deep hole scanning module is used for carrying out supplementary scanning on optical dead angles such as the edge of a preparation body and a deep caries cavity; lesion information such as decayed teeth and dental plaque is obtained through the fluorescence detection module; data splicing is assisted through the attitude sensing module, the data processing terminal fuses multi-modal data to generate a color digital model with high-precision three-dimensional morphology and pathological information labeling, and real-time guidance and quality evaluation are performed on a scanning process by using an artificial intelligence model. And full-automatic, high-precision and intelligent oral cavity three-dimensional data acquisition is realized.

The invention discloses a mass concrete temperature monitoring method. The method comprises the following specific steps: step 1, planning a monitoring point position; step 2, selecting and installing equipment; step 3, installing and pre-burying a measuring point, step 4, installing and pre-burying an optical fiber, step 5, laying temperature measurement layers, step 6, collecting and monitoring data, and step 7, ending data analysis. According to the invention, through a mode of combining a wireless temperature measurement process and manual measurement, the frequency of manual point measurement can be reduced, manpower consumption is reduced, the comprehensiveness of monitoring data is improved, the monitoring blind area of a traditional point type sensor is avoided, and the possible equipment fault problem of a temperature measurement system is avoided; and data comparison of manual monitoring and wireless monitoring is adopted, so that the reliability of temperature measurement data can be determined, and accident hidden dangers are reduced.

The invention relates to an automatic parking method and device based on a vehicle-mounted camera, equipment and a medium. The method comprises the steps that first automatic parking path information and an environment sensing image of an automatic parking camera are obtained, and automobile position information is obtained through inversion according to the first automatic parking path information and the environment sensing image; calculating the image quality weight of each environment perception image in the environment perception overlapping area; based on the environment perception image and the automobile position information, an automatic parking environment perception panoramic image is generated in combination with image quality weight; recognizing an obstacle, and if the obstacle enters the real-time blind area, generating obstacle danger alarm information; and in response to generation of the obstacle danger alarm information, correcting the first automatic parking path information based on the obstacle danger alarm information to obtain second automatic parking path information. By adopting the method, high-precision environment perception and dynamic positioning can be realized, and the adaptability of complex scenes and intelligent detection of blind area obstacles are improved.

An object detection system is configured for detecting an object within a front blind area of a vehicle. The object detection system may be configured to determine position coordinates of a plurality of candidate objects in front of a front panel of the vehicle, determine a visibility curve of a driver of the vehicle, and identify each candidate object as one of a visible object or an occluded object based on a comparison of the position coordinates to the visibility curve.