14104 results about "Targeted detection" patented technology

The present disclosure relates to the technical field of target detection, and particularly relates to a model deployment method, an end-side device and a storage medium, which are used for solving the problem in the related art of the accuracy of a deployed model being low. The method comprises: performing target detection on a video frame image input into a first model, and acquiring a first target detection result and a first confidence; if the first confidence is greater than or equal to a first confidence threshold value, recording the video frame image and the first target detection result as samples in a training set; if the first confidence is less than the first confidence threshold value, performing target detection on the video frame image on the basis of a second model, and recording the video frame image and an acquired second target detection result as samples in the training set; and training the first model on the basis of the training set, and replacing the current first model with a trained first model for subsequent target detection. In this way, the accuracy and model generalization capability of a first model are improved.

The invention relates to the technical field of computer vision and multi-modal learning, in particular to a weak supervision target detection method guided by cross-modal pseudo labels. According to the method, a labeled source domain data set is constructed to train an image classification teacher model, and a teacher-student network structure is constructed; clustering the regional features of the target domain image, allocating pseudo tags to each cluster by optimizing the allocation cost between the source domain category and the target domain cluster, and constructing a pseudo tag pool; and training a student model on the pseudo label pool for region feature detection of the target domain image. According to the method, a cross-modal attention mechanism is introduced, so that more accurate semantic alignment between a source category label and a target domain feature is realized; the stability of label distribution is improved by a structure keeping regular term; the generalization ability of the model is further enhanced by multiple rounds of pseudo-label confidence learning. The method can be widely applied to tasks such as target detection, cross-domain transfer learning and open world recognition, and efficient and accurate weak supervision target detection is realized.

The invention relates to the technical field of computer vision detection, in particular to an unmanned aerial vehicle target detection method based on frequency-space joint attention and dynamic fusion, and the method comprises the steps: obtaining an unmanned aerial vehicle image data set, carrying out the preprocessing, and dividing a training set and a test set; constructing a target detection model, inputting the training set into the target detection model to extract image features, sequentially performing frequency domain detail enhancement, spatial domain salient region extraction and multi-scale feature adaptive fusion based on the image features, and establishing a feature sequence; screening the feature sequence to obtain an initial target query, and finishing target classification and positioning on the initial target query through a decoder; training a target detection model by using the training set, and inputting the test set into the trained target detection model to generate a detection result; on the premise that the real-time reasoning advantage of RT-DETR is kept as much as possible, the problems that in an unmanned aerial vehicle scene, a target is prone to missing detection, the scale change is large, the background is complex, and the target is fuzzy are effectively solved, and the detection precision is improved.

The invention discloses a multi-modal visual fusion complex scene small target detection tracking method and system, and relates to the technical field of unmanned aerial vehicle target tracking, and the method comprises the steps: employing a visible light camera, an infrared thermal imager and a laser radar sensor which are carried on an unmanned aerial vehicle platform, and synchronously collecting RGB images, thermal infrared images and point cloud data; the consistency of the multi-modal data is ensured through data preprocessing and space-time alignment; constructing a lightweight double-branch network to extract multi-scale features, generating a fusion feature map by adopting adaptive weighted fusion, and generating depth information by utilizing point cloud to assist in scale estimation; a small target detection head is designed based on the fusion feature map, and precise detection is realized in combination with a feature pyramid network, adaptive scale prediction and a context awareness suppression mechanism; furthermore, through multi-mode cooperative tracking, including target association, spatio-temporal context modeling, trajectory prediction and a re-detection mechanism, tracking continuity is ensured.

The invention discloses an unmanned aerial vehicle image small target detection method based on dynamic filtering and an adaptive sparse Transform. According to the method, an end-to-end target detection framework is adopted, a dynamic filtering module is introduced into a backbone network, global feature interaction is achieved through data-dependent frequency domain operation, and linear calculation complexity is maintained. For feature interaction in a scale, an adaptive sparse Transform module is introduced to enhance the capability of focusing key information on high semantic hierarchy features of a model, and noise interference and feature redundancy are effectively suppressed at the same time. Through the combination of dynamic filtering and adaptive sparse Transform, the model can extract image foreground information more effectively on the premise of not significantly increasing the calculation burden, and the problem that a traditional target detection model is susceptible to complex background interference is significantly relieved.

The invention provides an anti-unmanned aerial vehicle intelligent identification and tracking system based on multi-source data fusion, and relates to the technical field of anti-unmanned aerial vehicle detection, and the system comprises a multi-source data preprocessing module which is used for outputting preprocessed multi-source data; the target detection module is used for carrying out unmanned aerial vehicle target detection on visual data in the preprocessed multi-source data and outputting a detection result containing a bounding box position, confidence and morphological characteristics; the target tracking module is used for performing unmanned aerial vehicle target tracking based on the target detection result and outputting a tracking result; and the fusion decision module is used for confirming the target identity based on the tracking result and the preprocessed multi-source data and outputting a final recognition result. The technical problems of low detection precision of small targets, difficulty in distinguishing similar targets, inaccurate 3D motion prediction, difficulty in re-identification after long-time shielding and the like in the prior art can be solved, and accurate identification, stable tracking and intelligent decision making of the unmanned aerial vehicle target are realized.

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.

The invention relates to the technical field of intelligent manufacturing, and discloses a target structure automatic detection method, device and equipment and a medium, and the method comprises the steps: obtaining scanning path planning data of a target detection structure, driving an ultrasonic probe to execute surrounding scanning motion, and collecting an ultrasonic image sequence and spatial pose data; dynamically adjusting a pressure application angle and a scanning speed based on force feedback information, fusing spatial pose data and an image sequence to perform three-dimensional reconstruction, constructing a three-dimensional geometric model of a target detection structure, extracting feature distribution data by applying an intelligent analysis model, generating a feature decision set, and performing feature extraction; and mapping the feature decision set to a three-dimensional coordinate system to construct an analysis report containing the feature type marks and the topological relation. Through fusion of force control scanning, image reconstruction and intelligent analysis, standardization and intelligence of a detection process are realized, image consistency and structure identification precision are improved, manual dependence is reduced, and comprehensiveness and reliability of lesion identification are enhanced.

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

The invention discloses a multi-modal collaborative security monitoring method, device and equipment and a storage medium, and relates to the technical field of computer vision and sensor fusion, the method comprises the following steps: collecting multi-modal data of a monitoring area, the multi-modal data comprising image data, infrared temperature data and environmental parameter data; based on the target detection model, detecting personnel security features and environment security features in the image data, and outputting a visual identification result and visual identification confidence; and when the visual identification confidence coefficient is lower than a preset threshold value and the consistency of the multi-modal data meets a synchronization condition, performing association verification of the multi-modal data by adopting weighted fusion in combination with the infrared temperature data and the environmental parameter data to perform secondary identification of a target so as to output the multi-modal fusion confidence coefficient. Through combination of visual identification and a multi-sensor fusion technology, accuracy and robustness of personnel and environment safety identification in a complex industrial environment are improved.

The invention discloses an abnormal scene detection method based on visual and semantic feature fusion, and relates to the technical field of safety monitoring and intelligent identification, and the method comprises the steps: carrying out the preprocessing of a collected original image, and obtaining a preprocessed image; forming a multi-modal input pair by the preprocessed image and a predefined structured prompt statement; inputting the multi-modal input pair into the visual language large model, and outputting semantic features including visual feature vectors and text vectors; inputting the preprocessed image into a target detection model, and outputting visual features; fusing the semantic features and the visual features through a cross-modal attention mechanism to obtain multi-scale fusion features; and inputting the multi-scale fusion features into detection heads of all scales, executing abnormal scene detection, and outputting an abnormal detection result. When the unconventional object is identified in the abnormal scene, the visual features and the semantic features are fused to perform abnormal scene detection, so that the strong perception capability of a complex scene is realized, and false alarm or missing alarm is effectively avoided.

The invention discloses a forest fire hidden danger identification method and device for a power transmission line based on a multi-modal large model, and the method comprises the steps: constructing a forest fire hidden danger identification model for a smog or flame-containing image outputted by a conventional target detection model, and enabling the model to guide a dialogue set through the combination with forest fire hidden danger identification, thereby achieving the recognition of the forest fire hidden danger. The dialogue context and the image visual features are fused, so that the deep fusion of the text semantics and the firework image features is realized; and finally, inputting the deeply fused features into a large language model, so that a forest fire hidden danger output dialogue of the power transmission line can be obtained by utilizing the deep semantic understanding capability of the large language model, and then a forest fire hidden danger recognition result of the power transmission line is obtained. Therefore, according to the invention, the judgment of whether the smoke and fire can damage the power transmission line is realized, the technology upgrade from smoke and fire existence detection to equipment threat research and judgment is completed, and on the basis, the problems of resource waste and untimely crisis response caused by reporting all smoke and fire information in the traditional technology can be avoided.

The invention discloses a converter station intelligent gateway image recognition system based on a YOLOv3 target detection algorithm, and the system employs a three-stage cooperative processing architecture design, and builds seamless connection of a multispectral image collection layer, an edge calculation gateway layer, and a cloud operation and maintenance management platform layer. The invention further provides an equipment defect detection method based on the system, bimodal image data are collected through the visible light camera and the thermal infrared imager, preprocessing operation is carried out, equipment positioning and defect classification are synchronously executed by utilizing the improved YOLOv3 network, a structured detection result is output, temperature field analysis is carried out on an infrared thermal image, and the equipment defect detection result is obtained. And an abnormal heating area is identified, when defects are detected, multi-level risk response early warning is generated, and a defect diagnosis report is pushed to the cloud operation and maintenance management platform layer. Real-time image analysis of converter station equipment can be realized, the method is suitable for automatic detection of typical fault defects of the equipment, and the operation and maintenance efficiency of a power grid is improved.

The invention discloses a container coating thickness online detection system and method, and the system comprises a non-contact thickness measurement unit which is used for transmitting modulation light to the surface of a container in real time according to the infrared light thermal modulation principle to excite a coating to generate a thermal wave signal, and determining the coating thickness through detecting the thermal wave reflection delay; the multi-mode executing mechanism is used for selecting a robot or a truss to carry the thickness measuring unit according to the specification of the container, and receiving a control instruction to drive the thickness measuring unit to move to a target detection point of the container according to a preset path; the three-dimensional positioning system is used for detecting the position deviation of the container entering the measuring station and feeding back the space coordinates of the container to the control system; and the control system is used for generating a motion path instruction according to the size of the container and a preset sampling rule, and receiving the coating thickness data of the thickness gauge in real time to generate a detection report, so that the precision, intelligence and high efficiency of container coating detection are realized.

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.

The invention discloses a reservoir safety intelligent inspection method and system based on YOLO and VLM fusion, and the method comprises the following steps: S1, multi-source data collection and preprocessing: employing an unmanned plane and ground equipment to collect image / video data, and carrying out the noise reduction, enhancement and space-time alignment processing of the data; s2, improving YOLO target detection: optimizing a network structure and a training strategy; s3, link analysis after VLM: target / scene association judgment is realized by adopting the VLM; and S4, report generation. The invention provides a reservoir safety intelligent inspection method and system fusing YOLO and VLM. Cross-modal semantic understanding, zero sample reasoning and video global analysis capabilities of a visual language large model are utilized, the visual language large model is used as a post-processing tool of YOLO and is fused with the post-processing tool to work in parallel, full-process intelligentization of target detection-semantic analysis-report generation can be realized, the existing technical problems are effectively solved, and the visual language large model has a wide application prospect. And the comprehensiveness of the hydraulic engineering safety monitoring system is enhanced.

The invention relates to the technical field of infrared target detection, and discloses a YOL0-based adaptive full-scale infrared target detection network, which comprises a trunk feature extraction network, a neck feature fusion network, a detection head network and a training optimization module, and is characterized in that all the modules are sequentially connected in series to form a complete detection link; the infrared image multi-dimensional feature extraction system is used for infrared image multi-dimensional feature extraction and comprises a convolution layer, an SPPF module and a C2MFE module which are connected in sequence, and the C2MFE module replaces a standard convolution layer in a traditional C2f module through multi-kernel feature extraction convolution (MFEEConv) to achieve multi-direction and full-scale feature capture; and the neck feature fusion network is connected with the output end of the trunk feature extraction network, comprises a multi-scale feature fusion module (MFFM) and a feature pyramid structure, and is used for enhancing the feature correlation of different levels. The adaptive full-scale infrared target detection network based on YOL0 can efficiently adapt to complex scenes such as low illumination and severe weather, and realizes cross-scene full-scale infrared target accurate detection.

The invention discloses a cross-modal target detection method based on learnable Fourier transform, and mainly solves the problem of insufficient fusion of a visible light image and an infrared image in a complex scene due to inter-domain difference in the prior art. According to the implementation scheme, the method comprises the steps that bimodal features are extracted through a double-flow CSPDarknet53 network; a target position guiding module is utilized to enhance target area representation and suppress background interference; the features are converted to a frequency domain, and amplitude texture information of the visible light image and phase contour information of the infrared image are adaptively enhanced through a learnable frequency domain feature enhancement module; suppressing noise through global filtering and then inversely transforming back to a spatial domain; and finally, outputting a target detection result of the multi-modal image by the detection head. According to the method, frequency domain physical characteristics are fully utilized, full complementation and adaptive fusion of cross-modal features are realized, the detection precision and robustness of vehicles, pedestrians and other targets under low-illumination and complex backgrounds are remarkably improved, meanwhile, high calculation efficiency is kept, and the method can be applied to the fields of automatic driving, intelligent monitoring and the like.

The invention discloses a multi-modal three-dimensional target detection method and device based on image-guided depth completion and multi-stage iteration fusion, and the method comprises the steps: firstly, predicting a dense depth map through an image-guided depth completion module by using the context information of an image, and carrying out the image-guided depth completion; the depth map is fused with a sparse depth map generated by the laser radar in a mask guiding manner, so that a high-quality complemented depth map is generated, and the accuracy of subsequent view angle conversion is improved; and then, through a multi-stage iterative fusion module, iterative fine-grained fusion is carried out on the converted image aerial view features and point cloud aerial view features, so that modal conflicts are effectively relieved, and the expression ability of fusion features is enhanced. According to the invention, through accurate depth information completion and efficient multi-modal feature fusion, the precision and robustness of three-dimensional target detection can be significantly improved, and especially the effect is more obvious when a long-distance target or a blocked target and other difficult targets are processed.

The invention discloses a method and a device for detecting abnormity of an electric power inspection image, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring the electric power inspection image; identifying and cutting the electric power inspection image based on the type of to-be-inspected equipment to obtain a target inspection image; performing multi-scale decomposition and extraction on the target inspection image to obtain scale features; mapping the target inspection image based on the scale features, and determining a target detection area image; performing enhancement processing on the target detection area image to obtain an enhanced image; and performing comparative analysis on the enhanced image and a normal image at the target detection area image to obtain an anomaly detection result. Through the method and the device provided by the embodiment of the invention, accurate anomaly detection of the electric power inspection image is realized.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

The invention relates to the technical field of data processing, and provides a target detection method and device, a model training method and device, electronic equipment and a medium. The target detection method comprises the steps that a to-be-recognized image and a query text are acquired, and the query text is used for querying a target object corresponding to the query text in the to-be-recognized image; performing image recognition on the to-be-recognized image to obtain image description features and region detection visual features; performing regional multi-modal fusion processing on the image description features and the regional detection visual features to obtain regional multi-modal fusion features; performing feature fusion processing on text features obtained based on the query text and the regional multi-modal fusion features to obtain text regional fusion features corresponding to the query text; and a target detection result is obtained based on the text features and the text region fusion features, so that the fusion degree of text semantics and image region features is improved, and the accuracy and robustness of target detection in a complex scene are improved.

The invention belongs to the technical field of ocean detection, belongs to a multi-sensor fusion intelligent anti-collision method and system, comprises a sensing layer, a processing layer and an application layer, and provides an intelligent anti-collision and evidence recording ocean monitoring floating system integrating computer vision, target ranging, satellite positioning and ship automatic recognition system multi-sensor fusion. According to the invention, YOLOv8 target detection, Transform data fusion and a Kalman filtering algorithm are adopted, so that accurate detection and anti-collision early warning of ships and floating objects on the sea are realized. The system has an AIS failure processing mechanism and an evidence encryption storage function, and ensures reliable operation and data compliance under complex sea conditions.

The invention discloses a remote sensing image-oriented multi-scale adaptive small target detection system and method. The system comprises a backbone network backbone, a feature aggregation network Neck and a detection head Head, the backbone network backbone is used for extracting multi-scale effective features, the feature aggregation network Neck is used for fusing and enhancing the multi-scale effective features, and the detection head Head is used for making a decision for target detection. The method comprises the following steps: constructing a target detection data set by using remote sensing images, preprocessing the images, and dividing the images into training, testing and verification sets; a backbone network backbone is adopted to extract multi-scale effective features, and a neck network Neck is adopted to refine the extracted features; carrying out mixed loss training by adopting NWD loss; and the detection head Head outputs the category and location of the target according to the results of the classification branch and the regression branch. According to the method, the loss of information in the transmission process is reduced, the background noise is inhibited, and the accuracy of small target detection of the remote sensing image is improved.

The invention relates to the technical field of target tracking, in particular to a photoelectric tracking algorithm and system for self-adaptive target tracking, and the system comprises an image collection module, a cross-spectrum self-adaptive sensing module, an image processing and target recognition module, a self-adaptive tracking algorithm module and a servo control module, and constructs a closed-loop feedback link to achieve the synchronous optimization of parameters in a whole link. The algorithm comprises the steps of image acquisition and adjustment, target detection, parameter optimization, servo driving and feedback. The system can accurately estimate a high-speed turning target, is adaptive to complex environments such as low illumination / haze and the like, completes sheltered target recapture within 0.5 s, has servo compensation precision of + / -0.1 degree, is suitable for airport bird monitoring, port unmanned aerial vehicle tracking and highway vehicle monitoring scenes, and significantly improves tracking stability and practical value.

The invention relates to the technical field of unmanned aerial vehicle detection and recognition, in particular to an unmanned aerial vehicle detection system and method based on vision, laser radar and sound waves, and the system comprises a sensor unit, a data processing fusion unit and a target recognition tracking unit. The data processing fusion unit performs multi-source data alignment and weighted fusion to extract texture, shape and color features of the image, point cloud generates a depth map, sound wave signals are mapped into a two-dimensional feature map, and weights are dynamically distributed based on sensor reliability to generate a unified feature map; the target identification and tracking unit identifies the type of the unmanned aerial vehicle by using the optimized deep learning model and realizes accurate prediction and updating of position and speed states in combination with Kalman filtering, and the decision response unit triggers sound-light alarm and wireless alarm for the non-cooperative unmanned aerial vehicle in real time and transmits target dynamic information to the ground station. And the target detection accuracy of the unmanned aerial vehicle in a complex environment is improved.

The invention provides a multi-small-target tracker and tracking method based on SAM2, and the method comprises the steps: dividing a video into a plurality of segments, and enabling the last frame of a previous video segment to be overlapped with the first frame of a next video segment; performing target detection on a first frame of the initial video clip, and allocating an ID to a detected target object; target tracking is executed in each video clip through SAM2, target detection is executed on overlapped frames between adjacent video clips, a detected bounding box is matched with a mask output by SMA2 through a previous video clip according to a mask-to-detection association strategy, and target tracking is executed in each video clip through SAM2; therefore, when a new target appears in the next video clip, a new ID can be allocated to avoid tracking interruption. According to the method, the mask and the detection are located at the same space-time position through video frame overlapping, so that tracking cannot be interrupted as long as the appearance of the target can still be visually distinguished, and the tracking failure rate when the size of the target is too small or the camera is zoomed and moved can be remarkably reduced.

The invention discloses an unmanned aerial vehicle electric power inspection obstacle avoidance method and system, and the method comprises the steps: firstly obtaining the multi-mode sensor data of an unmanned aerial vehicle, and carrying out the weighted fusion of the data, and obtaining the fusion data; generating a global path based on the fused data; and then generating a local obstacle avoidance path by using a dynamic window algorithm based on the global path. Then, carrying out path cooperation on the global and local obstacle avoidance paths to obtain an initial flight path; and after a real-time environment image collected when the unmanned aerial vehicle flies along the initial flight path is obtained, the target detection model is called to detect the image, and path obstacle avoidance information is obtained. And then adjusting the initial flight path according to the obstacle avoidance information to obtain a target flight path. According to the embodiment of the invention, the robustness is improved through the weighted fusion of the multi-modal sensor data, the global path is generated based on the fused data, the local obstacle avoidance path is generated in combination with the dynamic window algorithm to optimize the trajectory stability, and the obstacle is identified by using the model, so that the safety and efficiency of the unmanned aerial vehicle power inspection are enhanced.

The invention provides an intelligent driving control method, a vehicle and a storage medium, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring environment sensing data acquired by a sensor; the environment sensing data comprises rainfall detection data and target detection data; determining a real-time comprehensive rainfall level according to the rainfall detection data; dynamically adjusting the target detection weight of each sensor in data fusion according to the comprehensive rainfall level and the real-time target detection confidence of each sensor; based on the adjusted target detection weight, performing fusion processing on the target detection data collected by each sensor to obtain a target detection result; and adjusting an intelligent driving control strategy according to the comprehensive rainfall level and / or the target detection result. The environment sensing accuracy of the vehicle in a rainy environment is improved, the intelligent driving control strategy of the vehicle can adapt to the rainy environment change, and the driving safety and the driving experience are comprehensively improved from the sensing layer to the control layer.