50 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 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 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.

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.

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 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 invention relates to a vehicle safe driving auxiliary control method and device, equipment and a medium, and the method comprises the steps: partially introducing a C2fRepGhost module into a neck network of a first traffic sign detection model, so as to construct a second traffic sign detection model; the neck network receives a plurality of original feature maps output by the backbone network, performs cross-scale splicing on the plurality of original feature maps and features of other scales processed by the Upsample upsampling module through Concat operation, and inputs the plurality of features after cross-scale splicing into a C2fRepGhost module to determine a plurality of enhanced feature maps; and inputting the plurality of enhanced feature maps into a detection head network of a second traffic sign detection model to determine traffic sign categories and traffic sign key information corresponding to road traffic signs in the traffic scene image, and generating a corresponding driving assistance control instruction by an electronic control unit of the target vehicle, and sending the driving assistance control instruction to the vehicle. According to the invention, the detection speed is obviously improved while the detection precision of the traffic sign is improved.

The invention relates to the technical field of road traffic sign detection, and particularly discloses a lightweight-based traffic sign detection method, a storage medium and electronic equipment, and the method comprises the steps: obtaining road traffic image information; preprocessing the road traffic image information; the preprocessed image information is input into a traffic sign detection model for feature extraction, feature fusion and classification detection in sequence, the traffic sign detection model at least comprises a backbone network, a neck and a prediction module, the backbone network carries out feature extraction on the preprocessed image information layer by layer according to a multi-kernel convolution strategy and element-by-element multiplication, and the feature extraction is carried out; the neck part carries out feature fusion on the image features of different levels and obtains a plurality of feature maps of different scales on the basis of optimizing attention weight distribution, and the prediction module carries out classification according to the feature maps of different scales to obtain a traffic sign detection result. The light-weight-based traffic sign detection method provided by the invention has the characteristic of light weight, and integration of the traffic sign detection methods can be realized.

The invention provides a traffic sign detection method and device and computer equipment, and the method comprises the steps: inputting a historical traffic sign image into a YOLO initial model, and determining semantic sensitivity, category weight and scale sensitivity according to a detection tag and a semantic tag corresponding to the historical traffic sign image; generating an importance score of each channel in the feature network according to the semantic sensitivity, the category weight and the scale sensitivity; on the basis of the importance score, dividing each channel into a semantic core channel, a minority class channel and a redundant channel, and respectively configuring retention weights for the semantic core channel, the minority class channel and the redundant channel; dynamically cutting the redundant channel according to the reserved weight to obtain a YOLO detection model; and when it is monitored that the current image is input to the YOLO detection model, a traffic sign detection frame and a semantic decision result are output. According to the method, the reserved weights of the semantic core and the minority channels can be configured in a differentiated manner, and the redundant channels can be cut, so that the semantic decision consistency and the detection precision are comprehensively improved.

The application provides a kind of severe weather traffic sign detection method and system based on multi-level optimization and meteorological classification, belongs to the field of automatic driving environment perception.To solve the problem that the existing traffic sign detection method has poor module cooperativity under severe weather, the separation design of image enhancement and detection model leads to key sign details loss or noise amplification, weak dynamic adaptability and precision-efficiency imbalance.The application proposes a three-level optimization architecture AWEN-YOLO based on meteorological perception, through the adaptive enhancement and dynamic feature fusion mechanism guided by meteorological classification, realizes high-precision and high-efficiency traffic sign detection under severe weather, overcomes the limitations of traditional methods in bad weather monitoring;Experiments prove that under the adaptive enhancement and dynamic feature fusion mechanism guided by meteorological classification, high-precision and high-efficiency traffic sign detection under severe weather is realized, which meets the real-time perception needs of automatic driving system in complex meteorological environment.

The invention provides a traffic marker detection model updating method and device, equipment, a storage medium and a program product, which can be applied to the field of traffic. The method comprises the following steps: acquiring first position information and marker types of a plurality of first traffic markers, and a first false detection judgment result of each first traffic marker; determining a plurality of first objects to be continuously recognized based on the plurality of frames of traffic images and the first position information of the plurality of first traffic markers in each frame of traffic image; classifying the plurality of first objects to be continuously recognized based on the first false detection judgment results and the marker types of the plurality of first objects to be continuously recognized to obtain a plurality of marker class clusters; and determining a false detection training set from the plurality of marker class clusters, and updating model parameters of the first false detection discrimination model based on the false detection training set to obtain an updated first false detection discrimination model. According to the invention, the false detection rate of the traffic marker can be reduced.

The invention relates to a road condition detection system for detecting the surface condition of a road in front of an ego-vehicle currently traveling on the road, wherein the road condition detection system comprises: a signal acquisition means configured to acquire input signals resulting from a detection of the road surface condition in front of the ego-vehicle and from the detection of a traffic sign in front of the ego-vehicle; a processing means configured to process the acquired input signals and to obtain from the processing of road surface condition information as to whether the road surface condition in front of the ego-vehicle is abnormal or normal, and traffic sign information as to whether the acquired traffic sign indicates that the road surface condition in front of the ego-vehicle is abnormal; and an evaluation means configured to perform an evaluation.wherein the assessment indicates that the surface condition of the road in front of the ego vehicle is abnormal, if the traffic sign information indicates that the surface condition of the road in front of the ego vehicle is abnormal, and a means for setting an abnormal road condition, which is configured to set at least one control signal for changing at least one dynamic parameter of the ego vehicle relating to the dynamics of the ego vehicle, based on the assessment indicating that the surface condition of the road in front of the ego vehicle is abnormal. Furthermore, the invention relates to a corresponding computer-implemented method.

Embodiments of the present application provide a traffic sign detection method based on improved YOLOv8n and related devices. The method comprises: obtaining a traffic sign image; inputting the traffic sign image into a trained target detection network to obtain a traffic sign detection result, wherein the target detection network takes YOLOv8n network as a basic framework, replaces the Conv convolution modules of the 0th layer, the 1st layer, the 3rd layer and the 5th layer in the original main network of the YOLOv8n network with GCConv modules to obtain a target main network; replaces the original Detect detection head of the YOLOv8n network with an LSCD lightweight detection head; and replaces the original CIoU loss function of the YOLOv8n network with a Focaler-CloU loss function. Based on this, the embodiments of the present application can balance the detection accuracy and lightweight of the model, and meet the requirements of intelligent transportation systems for high performance and low delay.

The application provides a high-resolution traffic sign rapid detection method based on adaptive region screening, proposes an adaptive region screening model, extracts image features and uses image blocking technology to realize the purpose of judging whether each image block contains a traffic sign, the model comprises four parts of image adjustment, multi-scale feature fusion, region feature extraction and category prediction, proposes a traffic sign detection model based on image segmentation technology, a multi-scale semantic segmentation module CAM, extracts multi-scale features, realizes simple fusion, further extracts the fused features to obtain multi-scale segmentation graphs corresponding to different resolutions, introduces the above segmentation graphs into a multi-scale feature fusion network of the traffic sign detection model, guides and improves the information extraction of the foreground features, and Tex explores the background area of the features again after the fused feature output, and refines the input features again. The application can help the traffic sign detection to realize the balance between precision and real-time performance.