73 results about "Traffic sign detection" patented technology

The invention discloses a complex scene traffic sign detection method and system based on dynamic frequency band focusing and double-domain attention screening. The method comprises the following steps: carrying out data preprocessing and data enhancement on a collected road traffic sign image; a CADPCM module and a CHAttention cross coordination attention mechanism are used to construct a CACHNet backbone network; designing a DWMSN neck network, and establishing a dynamic fusion mechanism of multi-scale features; a CACHNet and a DWMSN neck network are used to construct a CDWN model, and a traffic sign enhancement data set is used to train the CDWN model to determine the optimal model weight thereof. Compared with the prior art, the method has the advantages that the average detection precision is improved by 3.3% while the light weight of the model is maintained by constructing a three-level framework of the feature extraction unit, the attention feature expression enhancement and the dynamic feature fusion, the complex scenes such as illumination variation and shielding can be effectively dealt with, and the method is suitable for popularization and application. And high-precision traffic sign detection support is provided for a vehicle-mounted intelligent auxiliary driving system.

The invention discloses an improved YOLOv11-based traffic sign identification and detection method, which comprises the following steps: based on a backbone network of YOLOv11, providing an IPC3k2 layer, replacing standard convolution by Ghboost to realize lightweight feature extraction, embedding a Coard coordinate attention mechanism in Bottleneck to strengthen space positioning capability, and combining a double-branch Ghboost feature fusion strategy; a SomSPPF module is provided for the trunk part, and a multi-scale deformable pyramid pooling module, a two-dimensional attention mechanism module and a SamReBlock re-parameterization convolution module are fused; in a neck network, an MSCATR attention module is added, dynamic channel fusion is adopted to realize dynamic fusion of channel statistical guidance, cross-space fusion is utilized to establish a space cooperation mechanism, an adaptive feature fusion module is introduced, and finally residual multi-level feature optimization is realized through learnable weight parameters. Compared with the prior art, the method has the advantages that the accuracy of traffic sign detection of the YOLOv11 model can be effectively improved, and the advantages of real-time performance and robustness of the model are considered.

The present application discloses a traffic sign detection method, including: obtaining an image of a traffic road to be detected; pre-processing the image of the traffic road; inputting the pre-processed image of the traffic road into a pre-trained traffic sign detection model, to obtain a classification result output by the traffic sign detection model; and marking a detected traffic sign based on the classification result of the traffic sign detection model, to output a marked traffic sign image.

The invention discloses an efficient frequency domain enhanced traffic sign detection method suitable for a snowy city scene, and belongs to the technical field of computer vision and intelligent traffic. According to the efficient frequency domain enhanced traffic sign detection method suitable for the snowy city scene, by designing a frequency domain compensation enhancement module, capture and fusion of multi-scale frequency domain information are achieved, and therefore the feature representation capacity and the model generalization performance are improved. According to the method, the YOLOv5 is used as a basic model for construction, the OfficientViT is used as a backbone network for feature extraction, the problem that the feature representation ability and generalization of a traditional model in snowy days are reduced is solved through a different-frequency feature complementation method, and meanwhile a loss function is optimized based on the Wasserstein distance and the thought of Gaussian reweighting. According to the method, different frequency features can be effectively complemented and enhanced, the feature representation capability in a snowy day scene is improved, and the provided FENet has good effectiveness and robustness in snowy day traffic sign detection.

Disclosed in the present invention is a traffic sign detection method, comprising: acquiring a traffic road image to be detected; preprocessing the traffic road image; inputting the preprocessed traffic road image into a pre-trained traffic sign detection model to obtain a classification result output by the model; marking a detected traffic sign on the basis of the classification result from the traffic sign detection model, and outputting an image of the marked traffic sign. The traffic sign detection model uses an improved RT-DETR model in which on the basis of an RT-DETR model, layers 5-7 are replaced with three downsampling feature extraction layers of a feature learning fusion module DualBlocks, wherein each replaced layer consists of a DualConv module, an average pooling module and an ReLu linear activation function. Layers 11 and 16 of an RT-DETR network model are separately replaced with a dynamic upsampling layer of a dynamic upsampling operator Dysample, wherein the dynamic upsampling layer consists of a sampling point generator, a sampling apparatus and an interpolation function. The present invention can reduce the number of parameters while improving the detection accuracy.

This disclosure relates to a traffic sign detection method and a training method for a traffic sign detection model, relating to the field of intelligent transportation technology, and particularly to autonomous driving technology. The traffic sign detection method includes: acquiring a target image containing traffic signs; and inputting the target image into a traffic sign detection model to obtain detection markers corresponding to the traffic signs; wherein the detection markers include at least one of detection points and detection lines, used to characterize the position of the traffic signs in the target image.

The invention discloses a complex environment-oriented multi-scale adaptive dynamic feature fusion traffic sign detection method, which comprises the following steps of: selecting and preprocessing a traffic sign data set, and dividing the data set into a training set, a test set and a verification set; a CPADM-YOLO model is constructed, the model is improved on the basis of YOLOv8, a backbone network adopts a C2f-CPAM module to replace a traditional C2f module, a neck network introduces a TFE module and a DZSF module to reconstruct a new neck network to enhance feature fusion and multi-scale target detection capability, and a loss function adopts WiseMPDIOU to replace an original loss function; and training the CPADM-YOLO model by using the processed data set, and evaluating the performance of the model through the test set. Compared with the prior art, the improved CPADM-YOLO model remarkably improves the accuracy and robustness of traffic sign detection and the adaptability to multi-scale small objects and dense objects in a traffic sign target detection task.

The invention relates to a road traffic sign detection method, device and equipment and a medium, and the method comprises the steps: embedding a focusing linear attention mechanism module in a feature pyramid path of a neck network of a preset first traffic sign detection model, and adding a P2 detection layer in a detection head network, updating a CIoU loss function of the first traffic sign detection model into a WIoU loss function to construct a second traffic sign detection model; inputting the to-be-detected traffic scene image into a second traffic sign detection model trained to a convergence state to determine traffic sign categories and traffic sign key information corresponding to one or more road traffic signs in the traffic scene image; and transmitting the road traffic sign type corresponding to the road traffic sign and the traffic sign key information to an instrument panel of the target vehicle, and performing voice broadcast on the road traffic sign type and the traffic sign key information to prompt a driver to perform safe driving. According to the invention, the positioning and identification precision of traffic sign small target detection is greatly improved.

The application discloses an automatic driving complex scene traffic sign detection method based on multi-channel image extraction and belongs to the technical field of image detection. The application solves the problem that the existing automatic driving method has low detection accuracy due to image loss, deformation and blur, acquires multiple to-be-detected traffic sign images in a complex scene, performs enhancement processing on corresponding channel images according to the traffic signs, and extracts feature regions from each traffic sign; each to-be-detected traffic sign image is recognized and detected through a convolutional neural network, the accuracy of traffic sign detection is ensured, multiple detection results are saved to form a traffic sign database, so that the current to-be-detected traffic sign image is matched with the traffic sign database after the above operation to obtain corresponding detection results; and the results are fed back to an automatic driving system to assist the automatic driving system in controlling a car to run according to traffic signs.

The invention relates to the technical field of computer vision and intelligent traffic, and discloses a traffic sign intelligent detection method and system based on deep learning, and the method comprises the steps: obtaining a traffic sign image of a road, and generating a preprocessed traffic signal sign image through marking and preprocessing; constructing an improved traffic sign recognition and detection model; inputting the preprocessed traffic signal sign image into an improved traffic sign recognition and detection model for training, and optimizing by using an improved loss function to obtain a trained improved traffic sign recognition and detection model; acquiring a to-be-recognized traffic sign image of a road in real time, and after data processing, inputting the to-be-recognized traffic sign image into the trained improved traffic sign recognition and detection model for traffic sign detection to obtain a traffic sign detection result; according to the invention, the real-time performance is ensured, and the detection precision of various traffic signs is obviously improved.

This invention provides a traffic sign recognition method and related apparatus. The method includes: acquiring real-time image data and environmental data collected during vehicle movement; concatenating the preprocessed environmental data into an environmental vector, and generating a modulation vector based on the environmental vector; applying channel attention modulation to a feature map using the modulation vector to obtain a modulated multi-scale image feature map; performing feature recalibration processing on the multi-scale image feature map to obtain a recalibrated feature map; performing traffic sign detection based on the recalibrated feature map and outputting an interpretable detection result for the traffic sign; and performing online incremental learning on the target detection model based on the interpretable detection result according to preset triggering conditions, and collaborating with a cloud server through federated aggregation. Based on this, this invention can intelligently recognize traffic signs, obtain interpretable detection results, and support online incremental learning.

The application discloses a traffic sign detection method fusing a Transformer mechanism, and comprises the following steps: step 1, according to the Transformer mechanism, an encoding module Trans combining global features of an image is designed; step 2, a feature fusion network is built by replacing an ordinary convolution module with a GhostConv, a light and convenient linear operation of the GhostConv is used to realize a light and convenient extraction process of redundant features, and calculation power and memory occupation are released; and step 3, a simplified decoupled detection head Slim Decoupled Head is designed, classification and regression tasks are separated and analyzed, and the output capacity of the network model is strengthened. The application can complete detection of small traffic signs in a complex road scene with high accuracy.

The application discloses a traffic sign detection method based on receptive field optimization and mixed convolution feature fusion, and the method comprises the following steps: obtaining a traffic sign picture and constructing an original data set; dividing the original data set into a training set, a test set and a verification set; constructing a network model, wherein the network model comprises three parts of a feature extraction network, a feature fusion network and a prediction output network; setting an initial learning rate, a learning rate decay mode and a training frequency of the network model according to the training set, optimizing network parameters by using an SGD optimizer, and training the network model; inputting a picture to be detected into the trained network model to detect the traffic sign, and outputting specific position and category information of the traffic sign in the picture to be detected. The embodiment of the application is based on the optimization of receptive field and the fusion of mixed convolution features, efficiently improves the precision and speed of the model, improves the accuracy of multi-scale target detection, and can be widely applied to the technical field of intelligent traffic.

This invention discloses a traffic sign detection method based on spatial completion and multi-scale focusing, comprising: performing category balancing enhancement on the traffic sign dataset to construct a balanced dataset; embedding a feature complementarity mapping module in the Q4 layer of the YOLOv12 backbone network, fusing color and spatial information through channel segmentation and bidirectional attention mechanism to complete the features of small targets; embedding multi-scale convolutional attention modules in the P3, P4 and P5 layers of the detection head respectively, using differentiated strip convolution kernels and color attention mechanism to achieve feature focusing of traffic signs at multiple scales; using a composite loss function combining SIoU loss and shape matching loss for network training optimization; performing color enhancement, multi-scale feature extraction and non-maximum suppression algorithm post-processing in the inference stage to output the traffic sign category, bounding box and confidence score; this invention significantly improves the accuracy of small target detection and multi-scale adaptability, meeting the real-time detection needs in complex traffic scenarios.

The invention discloses a traffic sign detection method and device and a storage medium. The traffic sign detection device obtains a traffic scene image; performing feature extraction on the traffic scene image through a backbone network in the trained detection model to obtain a multi-scale basic feature map; performing frequency domain feature adaptive enhancement on the multi-scale basic feature map through a preset neck network in the trained detection model and the brightness information of the traffic scene image to obtain enhanced features; and performing traffic sign detection on the enhanced features through a detection head in the trained detection model to obtain a traffic sign detection result. Based on the above scheme, the method can be adaptive to environmental changes, strengthens the key features of the traffic signs from the perspective of frequency domain, overcomes the detection performance bottleneck of a conventional detection method in a complex real scene, and effectively improves the robustness and detection precision of traffic sign detection.

The application discloses a traffic sign detection method based on an adaptive double-path local-global fusion network and belongs to the technical field of computer vision and intelligent traffic. Three innovative modules work together: the nonlinear adaptive feature extraction module adopts a wide-narrow parallel structure, combines an adaptive deformation activation function and a partial convolution mechanism, and enhances the capture ability of weak features; the dynamic channel calibration attention module realizes dynamic calibration of key channels through a local-global double-path interaction strategy and double-view attention fusion; and the local-global feature fusion module efficiently fuses multi-scale features based on multi-granularity parallel attention and reparameterization convolution technology. The application constructs an end-to-end detection network on the basis of a YOLO framework, significantly improves the precision and environmental adaptability of traffic sign detection, balances the calculation efficiency and resource consumption through light-weight design, and is suitable for high real-time requirement scenes such as automatic driving and intelligent traffic systems.

The invention discloses a real-time traffic sign target detection method based on YOLOv10 improvement, and belongs to the field of traffic sign target detection in an intelligent traffic system. In order to solve the problems that an existing model is large in calculation amount, weak in feature representation and poor in complex scene adaptability, an LESM-YOLO model is designed; the model is fused with three modules, namely, a Light Weight Backbone module, an ECFPN module and a v10-SEAM-Detect module, wherein the Light Weight Backbone is used for carrying out sampling through C2f-gConv, carrying out Down-sampling through Double Layer and carrying out optimal calculation and feature extraction through Feature Share Conv, and the V10-SEAM-Detect module is used for carrying out C2f-gConv and Down-Layer sampling through Feature Share Conv, and the V10-SEAM-Detect module is used for carrying out C2f-gConv and Down-Layer sampling through Feature Share Conv. The ECFPN uses multi-scale efficient convolution and global heterogeneous kernel selection to enhance information integration; the v10-SEAM-Deect introduces an SEAM module to improve feature space and semantic information, and efficient, accurate and strong generalization of traffic sign detection is realized.

The present specification relates to a kind of active traffic sign recognition method and device based on the fusion of multiple cameras. Including: in the vehicle driving, vehicle-mounted panoramic camera collects and identifies multiple signs in panoramic image, carries out tracking, and judges the confidence of multiple signs, the sign with confidence lower than preset threshold is input sorting module according to importance index and is sorted, and according to the importance index, it is sequentially set as current target sign;According to the relative position of current target sign in the change of vehicle driving, the target angle of long-focus camera is adjusted, and high-definition image is collected to carry out high-definition identification, the result of high-definition identification is fused with the result of preliminary identification, and traffic sign recognition result is generated. The present specification embodiment realizes the efficient combination of panoramic camera and long-focus camera in the process of traffic sign recognition, greatly improves the recognition performance, and then effectively expands the range of traffic sign detection and recognition.

The invention discloses a traffic sign and pedestrian joint detection method based on multi-task learning, and the method comprises the steps: constructing a multi-task learning framework, integrating traffic sign detection and pedestrian detection tasks into a unified model which comprises a shared feature extraction network and two task-specific branch networks; a joint optimization strategy is adopted, and loss functions of traffic sign detection and pedestrian detection tasks are optimized at the same time in the model training process; a data enhancement and annotation optimization method is applied, a training sample is generated through a data enhancement technology, and spatial correlation information between traffic signs and pedestrians is utilized to assist annotation work; in the feature extraction stage, features of traffic signs and pedestrians are fused by adopting a feature fusion technology, and joint detection is performed in combination with an attention mechanism to obtain a detection result. Through the shared feature extraction network and the joint optimization strategy, the consumption of computing resources is reduced, and the detection efficiency is improved.

The application relates to a traffic sign detection method based on a Transformer, belonging to the field of image processing, S1: preparing a pre-training data set, which is divided into a training set and a test set; S2: constructing a traffic sign detection model based on the Transformer, including an information enhancement module: performing information enhancement on an input image, outputting feature maps A1, A2, A3 and A4; a Muti-Scale Transformer module: performing feature extraction and multi-scale feature fusion on the feature maps A1, A2, A3 and A4, outputting a feature map B; a target detection module: fusing the feature map B, generating a feature map F, and detecting the category and position of the traffic sign in the feature map F; S3: training and testing the model, and using the converged model to perform target detection on a to-be-detected image, and the application improves the performance of small target detection in traffic signs.

A vision system for a motor vehicle includes: an imaging device (11) adapted to capture an image (30) of the surrounding environment of the motor vehicle, and a data processing unit (14) adapted to perform image processing on the image (30) captured by the imaging device (11). The data processing unit (14) includes: a traffic sign detector (31) adapted to detect traffic signs in the image (30) captured by the imaging device (11) by image processing; a decision part (35); and a traffic sign estimator (36) adapted to estimate validity information (37) of one or more traffic signs in the image (30) captured by the imaging device (11).

The invention discloses an extreme weather ultra-wide-angle traffic sign detection method and system based on cloud edge collaboration, and belongs to the field of computer vision and target detection, and the method comprises the steps: obtaining to-be-detected ultra-wide-angle traffic sign data, and carrying out the preprocessing and video stream splicing fusion; inputting the spliced and fused data into the trained traffic sign detection model to obtain a traffic sign detection result; wherein the training of the traffic sign detection model comprises the following steps: constructing an ultra-wide-angle traffic sign data set; building a traffic sign detection model, wherein the traffic sign detection model comprises a lightweight traffic sign detection module, a confidence judgment module, a cloud edge collaboration module and a detection result output module; and training the constructed traffic sign detection model by using the constructed ultra-wide-angle traffic sign data set to obtain a trained traffic sign detection model. According to the method, the problem of image distortion in an ultra-wide field of view is effectively eliminated, target detection is completed through cloud edge cooperation, and the robustness and adaptability of extreme weather detection are improved.

The invention relates to the technical field of intelligent traffic, in particular to a device and a method for recognizing traffic signs around a running vehicle in a low-light environment, and the method comprises the following steps: an original image data acquisition step: acquiring an original image data sequence of a surrounding scene when the vehicle runs in the low-light environment; an image data joint optimization step: carrying out joint optimization processing including noise removal and feature enhancement on the original image data sequence to obtain optimized image data; a traffic sign feature detection step: inputting the optimized image data into a traffic sign detection model based on an improved YOLO architecture to obtain preliminary detection data of traffic signs; a recognition result optimization and output step: performing time sequence consistency verification on the preliminary detection data, and outputting stable traffic sign recognition result data; the robustness and practicability of the whole system in a dynamic driving environment are improved.

The invention discloses a traffic sign detection method based on space completion and multi-scale focusing, and the method comprises the steps: carrying out the class balance enhancement of a traffic sign data set, and constructing a balanced data set; a feature complementation mapping module is embedded in a Q4 layer of the YOLOv12 backbone network, color and space information are fused through channel segmentation and a bidirectional attention mechanism, and small target features are complemented; a multi-scale convolution attention module is embedded in a P3 layer, a P4 layer and a P5 layer of a detection head respectively, and a differentiated stripe convolution kernel and color attention mechanism is adopted to realize feature focusing of multi-scale traffic signs; carrying out network training optimization by adopting a composite loss function combining SIoU loss and shape matching loss; color enhancement, multi-scale feature extraction and non-maximum suppression algorithm post-processing are carried out in the reasoning stage, and traffic sign categories, bounding boxes and confidence coefficients are output; according to the method, the small target detection precision and the multi-scale adaptive capacity are remarkably improved, and the real-time detection requirement in a complex traffic scene is met.

The application provides a traffic sign detection and recognition method based on a multi-information attention fusion network, designs an MIAF-Net model, and considers the precision and speed of detection. The method comprises the following steps: step one, constructing and initializing the MIAF-Net model: the MIAF-Net model comprises a backbone network built by a Conv module and a designed FCSP module, a neck network composed of an FPN feature aggregation network, a PAN feature aggregation network and a foreground perception attention module FPA, and a multi-scale information fusion detection head MIFH; step two, performing feature extraction on the input image by using the backbone network, fusing the multi-scale features output by the backbone network by using the neck network, decoupling the features by using the multi-scale information fusion detection head MIFH according to the fused features, and extracting, fusing and decoupling the features according to the set rules; step three, initializing the MIAF-Net model hyperparameters, training the model by using a road traffic sign training set, detecting and recognizing the road traffic sign by using the trained model, and outputting the result.

The invention discloses a traffic sign detection method based on single sample learning. The method comprises the following steps: constructing a training set, a verification set and a test set based on a traffic sign reference data set; for the training set and the verification set, adopting a pre-training-based open vocabulary target detection model to generate traffic sign candidate areas in the image, and carrying out intersection-to-union ratio screening on the traffic sign candidate areas to obtain traffic sign candidate areas related to the task and category labels; training the task-related traffic sign candidate areas and the category labels by adopting a single sample recognition algorithm based on metric learning to obtain a trained traffic sign detection model; processing the test image of the test set based on the trained traffic sign detection model to obtain a preliminary matching result; based on the preliminary matching result, a visual language model and semantic prompt guide words are adopted to obtain a semantic verification result, and based on the semantic verification result, a Kalman filtering and intersection-to-union matching strategy is adopted to obtain a final traffic sign detection result.

A multi-task automatic driving perception method based on task prompt comprises the following steps: constructing a multi-task automatic driving perception network comprising a segmentation all-in-one model (SAM) encoder in an offline stage, and training by adopting a multi-source data set sample comprising vehicle detection, traffic sign detection, traffic light detection and lane line segmentation; and performing real-time image detection by adopting the trained network in the online stage. According to the method, cross-domain and cross-dataset automatic driving expandable perception is realized through task prompt, and the increase of additional parameter quantity caused by multi-task parallel is reduced through a unified decoder in the task.

The present invention is related to a computer-implemented method for generating synthetic traffic sign data for training a traffic sign detection model. The method includes: extracting, from a sample image depicting a surrounding environment of a vehicle including a detected traffic sign, a face of the detected traffic sign, using information indicative of an orientation of the detected traffic sign; forming a 3D model of the traffic sign having the extracted face of the detected traffic sign; and generating synthetic traffic sign data by placing the 3D model of the traffic sign in one or more background images, wherein the 3D model is placed in the one or more background images based on a distribution of a placement of real-world traffic signs in a dataset of real-world images.

The present invention provides a traffic sign detection method and system for autonomous driving, comprising: obtaining a raw data stream in real-time autonomous driving, and preprocessing the raw data stream to obtain data to be detected; constructing a detection model, performing traffic sign detection on the data to be detected, and obtaining a detection result; processing the detection result and the raw data stream to generate data to be classified; constructing a classification model, extracting traffic sign categories and location information from the data to be classified, and obtaining a classification result; and combining a priori information database to post-process each target in the detection results and classification results to obtain a final detection result of the traffic sign. The present invention adopts a multi-stage processing method for small target detection, which not only solves the problem of computing resource occupation but also improves the detection effect. Assisted by data closed-loop optimization, the long-tail problem is greatly alleviated. Through the priori-based post-processing method, the safety redundancy when applied in the field of autonomous driving is improved.