105 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 invention relates to the technical field of target detection, in particular to a traffic sign defogging detection method based on improved MSR-YOLO, and various traffic sign pictures in a foggy environment are collected by using vehicle-mounted monitoring. A multi-scale adaptive network module MS is used for carrying out defogging preprocessing, the multi-scale adaptive network module MS improves image quality and detail representation through three different-scale convolution kernels, and then defogging processing is carried out through a dark channel prior defogging filter, a Gamma filter and a sharpening filter in sequence. Based on YOLOv11 network improvement, a C3K2-RFAConv module is introduced into a Backbone part and a Neck part to replace a C3K2 module, RFAConv of the Backbone part replaces a traditional convolution module, a traffic sign detection network model is obtained, and a data set is used for training the traffic sign detection network model. And carrying out target detection on the defogged image by using the trained traffic sign detection network model. Compared with the prior art, the influence of the foggy environment on traffic sign recognition can be effectively reduced, so that the foggy traffic sign detection accuracy is improved.

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.

The invention provides a traffic sign detection method and system for a low-light environment, and belongs to the technical field of automatic navigation. According to the method, the image in the low-light environment is decomposed into a plurality of processing tasks, a multi-modal feature extraction, cross attention and self-attention mechanism is adopted to accurately extract various feature information, the feature weight is dynamically adjusted, the high-quality enhanced image is generated, and the image quality is improved. Therefore, the accuracy of traffic sign target detection and the illumination adaptability of the system in a low-illumination environment are remarkably improved, the target recognition capability of an automatic driving system in a low-illumination scene is further improved, missing detection and false detection caused by insufficient illumination are reduced, and safe operation of vehicles under complex illumination conditions such as night and tunnels is guaranteed.

The invention discloses a traffic sign target detection method based on improved CPBM-YOLO, and belongs to the field of traffic sign detection. A CPBM-YOLO model is constructed, improvement is carried out based on a YOLOv8 model, and a C2f module of the YOLOv8 model is replaced by a CSP-PMSFA module in a backbone network part; the neck network part is introduced into an MAF-YOLO network structure, an SAF module and an AAF module of the MAF-YOLO network structure are replaced with BIFPN modules, a RepHELAN module is replaced with a C2f module, and the improved MAF-YOLO network structure is formed to serve as a neck network. Compared with the prior art, the performance and adaptability of the model in the aspect of traffic sign target detection are greatly enhanced, the improved method is practically applied to roadside traffic sign detection scenes, compared with an original YOLOv8 method, the improved method has all obvious advantages, the detection precision is improved, the parameter quantity is effectively reduced, and the calculated amount is remarkably reduced; the calculation speed is greatly increased, strong competitiveness is shown in the industry, and further development of the traffic sign detection technology is expected to be promoted.

The invention relates to an active traffic sign identification method and device based on fusion of multiple cameras. Comprising the steps that in the vehicle driving process, a vehicle-mounted panorama camera collects and recognizes a plurality of marks in a panorama image, tracking is conducted, the confidence degree of the marks is judged, the marks with the confidence degree lower than a preset threshold value are input into a sorting module to be sorted according to importance indexes, and the marks are sequentially set as current target marks according to the sorting of the importance indexes; and adjusting the target angle of the long-focus camera according to the changed relative position of the current target sign in the vehicle driving process, collecting a high-definition image for high-definition identification, and performing data fusion on a high-definition identification result and a preliminary identification result to generate a traffic sign identification result. According to the embodiment of the invention, the method achieves the efficient combination of a panoramic camera and a long-focus camera in a traffic sign recognition process, greatly improves the recognition performance, and effectively expands the detection and recognition range of traffic signs.

The invention discloses an ultra-wide-angle traffic sign detection method and system based on adaptive dynamic pruning, 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 splicing and 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; a traffic sign detection model is established and comprises a human eye attention concentration position detection module, a detection module based on dynamic pruning 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 image complexity is evaluated in real time, the calculation path of the model is flexibly adjusted, the allocation and use of calculation resources are optimized, and the sensing and processing capability on a large-view-angle target in a complex dynamic scene is improved.

The invention discloses a traffic sign detection method, which performs traffic sign detection based on full-dimensional dynamic convolution and attention guidance feature fusion, and specifically comprises the following steps: A1, image acquisition: acquiring image data containing traffic signs through an image acquisition device; a2, feature extraction: enabling the image data to pass through an improved LTS-YOLOv10 backbone network; a3, feature fusion: adopting an attention-guided bidirectional feature pyramid network (EMA-BiFPN) to optimize a feature fusion process; a4, traffic sign detection and classification; and A5, result output. And the accuracy of traffic sign detection is improved.

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.

The invention provides a traffic sign detection method and device, electronic equipment and a storage medium, and is applied to the technical field of vehicles, and the method comprises the steps: obtaining a vehicle coordinate and a target image feature of a vehicle driving region image, and inputting the target image feature into a traffic sign detection model, obtaining sign information and sign coordinates of traffic signs in the vehicle driving area image, inputting target image features into a traffic sign semantic segmentation model to obtain a foreground segmentation result of the vehicle driving area image, and inputting the sign coordinates and the foreground segmentation result into a multi-layer perceptron to obtain target sign coordinates of the traffic signs; obtaining the distance between the traffic sign and the vehicle according to the target sign coordinate and the vehicle coordinate, and outputting the sign information of the traffic sign under the condition that the distance between the traffic sign and the vehicle meets the distance judgment condition, so that the traffic sign related to the vehicle can be accurately obtained.

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 provides a traffic sign detection method and device based on clustering analysis, and belongs to the technical field of traffic sign detection. The method comprises the following steps: preprocessing a multi-disturbance traffic sign data set, and determining a first centroid of the data set; calculating a non-connectable data set based on the data set, and sorting the first centroids to obtain a second centroid; and updating the data set by using the non-connectable data set to obtain a first data set, pulling data points of the first data set to a second centroid, performing updating, aggregation and separation processing on the data after data translation based on the first data set to obtain a second data set, and performing data clustering to obtain a data clustering result. The method comprises the following steps: acquiring an optimal unsupervised contour coefficient, marking unmarked original data on the basis of a clustering result of the optimal unsupervised contour coefficient, obtaining a marked multi-disturbance traffic sign data set, carrying out model training, identifying a to-be-identified traffic sign by adopting a trained traffic sign detection model, and determining the type of the traffic sign. According to the invention, data interference in a traffic sign identification process can be reduced.

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 present invention discloses a traffic sign detection method and system based on YOLOv5. By using an online and offline hybrid data enhancement method to achieve data set amplification, designing a C2fGhost module and reconstructing a CSPDarkNet feature extraction network based on the module, it is possible to obtain richer gradient flow information while achieving lightweight; using shallow features P2 to replace deep features P5 for prediction, improving the model's small target detection capability; introducing Efficient‑RepGFPN as a feature fusion network to improve the model's multi-scale detection capability; using Wise‑IoU V3 loss function to replace the original CIoU loss function as a positioning loss function, improving the model's training effect on low-quality data sets; using confidence scaling distillation to further enhance the model's reasoning performance; performing INT8 quantization on the distilled model, significantly reducing the model's parameter amount and calculation amount. The present invention has high small target detection performance and reasoning speed, and has good real-time reasoning performance on performance-constrained platforms such as vehicle-mounted platforms.

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 provides a method based on multi-scale channel fusion (MCF), which is used for improving the detection precision of a traffic sign in a high-resolution image. Through the MCF module and the algorithm, attention to spatial locality is enhanced, more comprehensive feature information is obtained in channel dimensions, important information can be screened from fused multi-scale features, the sensitivity of target detection is improved, and especially, the detection effect on small targets or complex scenes is better. Meanwhile, a new P2 detection layer is introduced, fine features are captured through a shallow layer, key information is reserved by combining local and global pooling, the feature expression ability is optimized, and the overall detection effect is improved. According to the method, the detection precision is improved, and meanwhile, the deployment difficulty of the model is reduced.

The present invention discloses a traffic sign detection method based on multi-scale feature fusion, including S1: constructing a data set, which includes a number of traffic sign pictures and corresponding labels; S2: constructing a detection model, importing the pictures in the data set in S1 into a color difference feature extraction module to obtain color difference features; S3: importing the color difference features in S2 into backbone feature extraction to obtain extraction features of different scales; S4: importing the extraction features in S3 into a multi-view collaborative fusion module to obtain multi-view assisted fusion features; S5: performing residual connection on the multi-view collaborative fusion features and the color difference features and inputting them into a detection head to obtain a prediction output; S6: optimizing model parameters. The present invention performs residual connection on the color difference features and the multi-view collaborative fusion features, thereby performing image completion in the recognition process by combining the color information and visual information of traffic sign pictures, so as to obtain more accurate detection results.

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.