54 results about "Traffic sign recognition" patented technology

The invention discloses a lightweight traffic sign recognition method fusing time sequence characteristics in a shielding scene, and relates to the field of computer vision and intelligent traffic, in particular to a traffic sign recognition technology. Respectively constructing a single-frame data set with slight occlusion and a time sequence data set with severe occlusion; for a street scene image containing a complex background, training a lightweight target detector YOLOv8-N based on an existing bounding box label in a data set; secondly, a depth separable convolution structure with MobileNetV2 as a trunk network is adopted, and a high-dimensional feature map is extracted from the candidate mark image; then, a space attention mechanism is introduced, weighted correction is carried out on input features, an unshielded region is strengthened, and a shielded and noise region is suppressed; and finally, extracting a feature sequence of a historical frame of a heavily shielded sample, and inputting the feature sequence into an extended long-short-term memory network xLSTM for time sequence fusion.

The application discloses a night traffic sign recognition method and system based on deep learning and a storage medium. The application relates to the technical field of intelligent traffic, and is built based on an existing deep neural network to form a night traffic sign recognition model, so that the night traffic sign can be accurately and quickly recognized in a night environment. The night traffic sign recognition method based on deep learning proposed by the application enhances the adaptability of the model to the night environment through image preprocessing and improved backbone network, effectively alleviates the problems of false detection and missed detection of the model to the traffic sign in the night environment, improves the recognition accuracy of the model, and ensures the recognition speed.

The application discloses a traffic sign recognition method and system for active safety of commercial vehicles, and specific steps of the recognition method are as follows: S1, capturing road panoramic image information through a vehicle-mounted camera device, and performing pretreatment operation on the road panoramic image information, and setting a threshold value through color transformation space to extract an image of a region of interest; S2, matching the image of the region of interest with a traffic sign template library picture, and positioning a sign image in the road panoramic image according to a matching result; S3, designing and building a neural network model, training the neural network model on a sampling traffic sign data set, and obtaining an optimal traffic sign recognition model; S4, merging a multi-branch structure in the optimal traffic sign recognition model, and obtaining a remodeling model; S5, performing classification recognition on the sign image in step S2, inputting the sign image into the remodeling model in step S4 after normalizing the sign image, and judging a traffic sign type according to an output result.

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.

The invention relates to a method for controlling a driver assistance system in a road vehicle (22) having an electronically controllable drive motor (24) and an electronically controllable service brake system having wheel brake cylinders (30, 32, 34, 36) which can be actuated with pressure medium, and having a camera-based vehicle recognition, pedestrian recognition and traffic sign recognition, as well as a speed regulation function and an emergency brake function, wherein, when the road vehicle (22) approaches an identified bus stop or tram stop (2), the controller (26) of the driver assistance system, in addition to generating and outputting a warning signal to the driver of the road vehicle (22), also carries out at least one further measure to avoid a collision with a pedestrian (14.1, 14.2, 14.3). As an additional measure, according to the invention, the wheel brake cylinders (30, 32, 34, 36) of the service brake system of the road vehicle (22) are pre-filled with a predetermined fluid pressure before reaching the bus stop or tram stop (2) in order to prepare for a possible emergency brake. The invention also relates to a device (40) for carrying out the method.

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.

This invention proposes a traffic sign light-sensing algorithm, device, and system based on an improved HSV (High-Speed ​​Vector Characteristic). The light-sensing algorithm includes: using adaptive HSV threshold adjustment combining global brightness statistics and local block segmentation to generate a dynamic HSV threshold suitable for the current scene; and using this dynamic threshold to perform color segmentation on the image to obtain the target color region. Compared to the inherent HSV threshold, or solely global brightness statistics, or solely local block adaptive threshold correction, this overcomes the interference of different reflectivities and absorbances of objects in the environment, which cause interference in color recognition of different regions of the image under complex lighting conditions such as strong light, weak light, backlight, and local shadows. This significantly improves the robustness and accuracy of traffic sign recognition in dynamic, unstructured scenes.

The invention provides a traffic sign target recognition method based on deep meta-learning, a medium, equipment and a product, and relates to the technical field of meta-learning, and the method comprises the steps: obtaining a traffic sign image, and dividing the image into a training task set and a test task set; the method comprises the following steps: constructing a deep meta learning model by taking CNN as a meta network of meta learning and a structure of each task network, optimizing an MAML meta learning algorithm by using a ZeroTrick strategy on the basis of Meta-SGD, and setting a linear layer weight parameter as 0 after the algorithm starts and each sampling task cycle ends; and training the deep meta-learning model by using the training task set, testing the trained deep meta-learning model by using the test task set, and using the tested model for traffic sign target identification. According to the invention, the generalization ability and accuracy of the traffic sign recognition model under the condition of few samples are improved.

Embodiments of the present application provide a traffic sign recognition method, an electronic device, a storage medium and a program product. The method comprises: obtaining attribute information of multiple traffic signs on the same road from map update data; determining corner point road coordinates and sign face projection areas of the multiple traffic signs in a road coordinate system according to the attribute information; performing clustering processing on the multiple traffic signs according to the corner point road coordinates and the sign face projection areas, to obtain multiple target clustering clusters; for each target clustering cluster, determining a target type of a target traffic sign based on the corner point road coordinates and the sign face projection areas of the target traffic sign included in the target clustering cluster; and the target type is a side-by-side combined type or a composite combined type. The method is used to improve the recognition accuracy of traffic signs.

The invention provides a traffic sign recognition test system and method, a medium and equipment, a supporting base, a telescopic rod, a rotating support, a signboard frame base and a double-sided signboard are arranged, the telescopic rod is arranged on the supporting base, the rotating support is arranged at the end, away from the supporting base, of the telescopic rod, and the signboard frame base is arranged on the rotating support; the rotating support is used for driving the signboard frame base to horizontally rotate relative to the supporting base, the double-face signboard is arranged on the signboard frame base, and different traffic signs are arranged on the two faces of the double-face signboard respectively. By arranging the entity signboard, the electromagnetic interference is reduced, so that the test accuracy is improved, and by arranging the rotatable double-sided signboard, the test efficiency and convenience can be improved.

The invention relates to a vehicle (2) with a system (1) for camera-based traffic sign recognition, in which, during driving operation, an actual image of the traffic sign (B) is displayed by means of a camera (5). ist ) is detectable, wherein the system (1) is based on the detected traffic sign actual image (B ist ) a speed recommendation (v E ) for the driver. According to the invention, the system (1) has an analysis module (13) which, in the event of a driver-initiated speed correction measure that leads to a deviation from the speed recommendation (v E ) differing actual speed (v ist ) performs an error analysis to check whether or not there is a fault in the traffic sign recognition system (1).

The embodiment of the invention provides a traffic sign recognition model training method and device, electronic equipment and a computer readable medium. The training method of the traffic sign recognition model comprises the following steps: acquiring image data of a traffic sign based on traffic sign images acquired by a vehicle-mounted camera under different weather conditions; transform mapping is carried out on the image data to obtain enhanced data, and the transform mapping mode comprises at least one of rotation, miscutting, translation and zooming; and training the model based on the enhanced data.

This application provides a method, apparatus, control device, storage medium, and product for traffic sign recognition, belonging to the field of artificial intelligence technology. The method first enhances the acquired road image. This enhances the contrast, saturation, and brightness of the area containing the traffic signs in the first road image, creating a strong contrast between the traffic signs and the image background. This facilitates the feature extraction network in capturing effective semantic information, thereby simplifying the recognition of traffic signs in the road image. Then, based on the enhanced road image, a sign recognition model is used for recognition. Since the traffic sign recognition model employs a lightweight convolutional neural network (CNN), which has fewer parameters and lower computational complexity, its operating efficiency and inference speed can be improved, enabling real-time traffic sign recognition and thus enhancing the safety of autonomous driving.

The invention provides a traffic sign recognition method and device and a storage medium, and belongs to the technical field of sign recognition, and the method comprises the steps: importing a plurality of original traffic sign pictures, carrying out the preprocessing of all original traffic sign pictures, and collecting the preprocessing results to obtain a preprocessed traffic sign picture set; dividing the preprocessed traffic sign picture set into a traffic sign training set and a traffic sign test set according to a preset proportion; and constructing a training model, and training the training model through the traffic sign training set to obtain a to-be-processed model. The problem of traffic sign recognition in severe weather can be solved, the reliability and safety of an automobile auxiliary driving system in a complex environment are improved, the life safety of drivers and passengers is guaranteed, high-quality development of the automobile industry and the intelligent network connection technology is promoted, and key technical support is provided for intelligent traffic system construction.

The invention discloses a traffic sign recognition method based on a random Fourier feature visual state space model, and belongs to the technical field of computer vision, deep learning and intelligent traffic systems. The objective of the invention is to solve the problems of spectrum deviation, poor multi-scale adaptability and lack of uncertainty perception of the existing visual state space model. A hierarchical probabilistic network architecture (MS-RFF-VSSM) is constructed, a traffic sign image is mapped to a high-dimensional random feature space by using a kernel approximation technology through a hierarchical variational random Fourier feature embedding module, and meanwhile, the uncertainty of prediction is explicitly quantized based on a Bayesian framework; secondly, designing a multi-scale attention fusion module and a multi-scale receptive field visual state space backbone network, and considering global context modeling and adaptive extraction of local multi-scale features while keeping linear calculation complexity; and finally, carrying out dynamic weighting on the sampling features through a class attention prediction module to output a classification result. According to the method, the recognition precision and robustness of the model in a complex dynamic environment can be remarkably improved under the condition of relatively low parameter quantity, and the balance between the precision and the calculation efficiency is realized.

The invention relates to a method for improving the detection of road users who are outside the detection range of a sensor device of a vehicle for controlling an ADAS / ADS system, comprising the following steps: - detecting sensor data (220) of a traffic scene and surroundings of a vehicle (10) with an ADAS / ADS system (750) by sensors of a sensor device (220) of the vehicle (10); - Preprocessing the sensor data (220) and / or the fused sensor data (250) by an input layer (430) of an AI model (410) of the evaluation module (400), in particular for noise reduction and generating preprocessed sensor data (435); - Processing the preprocessed sensor data (435) by a first task layer (440) with an algorithm for detecting and tracking other vehicles, a second task layer (450) with an algorithm for traffic sign recognition, a third task layer (460) with an algorithm for predicting the trajectories of other vehicles, and a fourth task layer (470) with an algorithm for detecting road users such as pedestrians or animals to create processed input data (510, 520, 530, 540) for a decision model (500) - Merging processed input data (510, 520, 530, 540) into a fused context.

A safety warning device for deactivated driver assistance systems, hereinafter referred to as SWEFF, is characterized in that, when driver assistance systems (e.g., ABS (Anti-lock Braking System), TC (Traction Control), Slide Control, Wheelie Control, Engine Brake Control, ESP (Electronic Stability Program), Traffic Sign Recognition, Emergency Brake Assist, Lane Keeping Assist, etc.) are deactivated, the entire display or parts of the display flash (at least once) and / or an acoustic signal sounds at least once and / or a voice output is given at least once (e.g., via loudspeaker and / or a communication system in and / or on the helmet). The flashing and / or the acoustic signal and / or the voice output can be repeated after a certain period of time.This repetition can be prevented by the driver through various actions (e.g., pressing a control button and / or touching a touchscreen (e.g., display) and / or by voice input (e.g., via a microphone) and / or in a menu (software)). In vehicles with analog instruments, the instrument lighting or parts of the instrument lighting and / or at least one indicator light may flash. There may then be a separate indicator light for each driver assistance system.

The invention relates to the technical field of image recognition, and discloses a local occlusion traffic sign recognition method based on an attention enhancement CNN, and the method comprises the following steps: carrying out the dynamic occlusion data enhancement processing of an input traffic sign image, and obtaining an enhanced traffic sign image; inputting the enhanced traffic sign image into a lightweight convolutional neural network; the lightweight convolutional neural network performs primary feature extraction, intermediate feature fusion and advanced feature abstraction in sequence through a pyramid type multi-stage feature decoupling module; in the advanced feature abstraction process, hole depth separable convolution is adopted to process the feature map; and outputting the prediction probability of each type of traffic sign through the last full connection layer to obtain an identification result. According to the method, through dynamic occlusion data enhancement processing, block occlusion, Gaussian noise occlusion, geometric transformation and luminosity transformation are applied to the input image in the training stage, so that the stable recognition capability of the partially occluded image is kept in the reasoning stage.

The application provides a road traffic sign recognition method based on image semantic understanding, which comprises the following steps: image acquisition based on an image acquisition device, pretreatment of the collected image, construction of a road traffic sign data set, construction of a road traffic sign detection model based on a Blip network, training of the model based on the road traffic sign data set, obtaining of the trained road traffic sign detection model, and effect test of the trained road traffic sign detection model based on the road traffic sign data set. The application can recognize road traffic signs and generate sentence descriptions of the signs from the aspects of color, shape and composition, recognize lane types from a macro perspective, avoid dependence on annotated signs, realize efficient and accurate traffic sign recognition in a complex environment, and have high practical value.

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 relates to the technical field of machine vision, in particular to a traffic sign automatic identification method and system based on machine vision. After it is detected that a target traffic sign is shielded or damaged, loading multi-source image data of different visual angles and different time, obtaining a preset category label, extracting a sign area image, and obtaining a preliminary feature vector through a feature extraction network; spatial position parameter codes are combined to form position vectors, the position vectors are spliced, and complete feature representation is output through an attention fusion module; inputting a pre-trained condition generation type reconstruction model to generate a non-occlusion complete identification image; and if the reconstruction confidence reaches the standard, outputting a reconstructed image and a category label for traffic decision making. And the integrity, the accuracy and the robustness of traffic sign recognition in a complex road environment are greatly improved.

The invention discloses a test case generation method based on double-constraint decoupling diffusion, and aims to solve the problems that a test sample generation method of an automatic driving traffic sign cannot completely decouple semantic information and interference information of a traffic sign graph, and when the number of test samples is extremely small, the expansion efficiency is extremely low, the quality is poor, and the efficiency is high. Therefore, the test accuracy of the automatic driving traffic sign recognition model is low, and the automobile is easy to break rules or collide. According to the method, an original traffic sign image is preprocessed, a framework of an encoder, a MINE module, comparison decoupling, an expansion module and a diffusion model is built, accurate independence of semantic factors and interference factors is achieved through dual-constraint decoupling training of the MINE module and the comparison decoupling, and then the semantic factors and the interference factors are expanded by using the expansion module based on test requirements. A preliminary test sample is generated in combination with a diffusion model, and finally an effective test sample library is output through three-stage screening. The invention belongs to the field of test case generation.

The invention relates to a method for the automated execution and evaluation of HMI (Human-Machine-Interface) tests for information systems in a hardware-in-the-loop (HiL) process, comprising the following method steps: - Capture (S10) real-time images (30) of the HMI interface (10) during the execution of a test case (470), for example for traffic sign recognition by a driver assistance system, wherein the real-time images (30) contain objects (40) such as traffic signs, supplementary signs or symbols on a display of a display unit (20), by at least one camera of a camera module (200); - Processing (S20) the captured image data (220) by an image processing system (300) using a pre-trained neural network (350) to extract and classify the objects (40) shown in the real-time images (30); - Comparing (S30) the detected objects (40) and their positions on the display of the HMI display unit (20) with expected test results (480) of the test case (470), - Creating (S40) evaluation results (370) based on the comparison performed with analyses of deviations and similarities to verify and / or validate the performance of the HMI interface (10).

The invention discloses a traffic sign recognition method and system, and relates to the technical field of traffic sign recognition, and the method comprises the steps: carrying out the defogging image enhancement of a traffic sign image under the influence of heavy fog weather through a dark channel prior algorithm, and obtaining an enhanced image; segmenting the enhanced image by using an RGB (Red, Green and Blue) color model to obtain a preliminary segmentation region; performing morphological analysis on the preliminary segmentation region by using improved morphology to obtain a target region; pre-processing the target area, and performing primary matching on the pre-processed target area and the template library by using an absolute difference value and an SAD algorithm to obtain a matching result; and performing secondary matching on the matching result and the template library by using a normalized cross-correlation NCC algorithm to obtain an identification result. According to the invention, the traffic sign can be accurately identified under complex weather conditions.

The invention provides a whole vehicle intelligent cabin TSR enhanced pattern recognition system and method based on AI, and the system is based on an intelligent cabin controller, and comprises an image obtaining module which is used for obtaining a traffic sign image through a whole vehicle front camera; the preprocessing module is used for preprocessing the acquired traffic sign image, including graying, noise reduction and enhancement operations, and specifically relates to basic enhancement, dynamic adaptation of special scenes and model iterative optimization; the pattern recognition module is used for inputting the preprocessed image into an AI-based pattern recognition model, performing feature extraction and analysis and outputting a recognition result of the traffic sign; and the post-processing module is used for post-processing the recognition result, judging the reliability of the recognition result according to the confidence coefficient, and further processing an unreliable result or prompting a driver. According to the invention, the accuracy and reliability of traffic sign recognition can be improved, more accurate traffic sign information is provided for a driver, and the driving safety is 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 recognition method based on YOLO11, and the method comprises the following steps: S1, collecting a multi-source road scene image, carrying out the preprocessing, and forming a small-target enhanced data set; s2, based on the small target enhanced data set, using a CSPDarknet backbone network of YOLO11 to carry out input feature extraction on the traffic image; s3, carrying out multiple times of CBS processing and C3K2-DF module processing on the input features, extracting a first feature and a second feature, and carrying out modeling through an SPPF module and an MA module to obtain a third feature; s3, performing CBS processing on the features, and performing feature fusion and enhancement through three modules in sequence to generate three groups of final fusion features; and S4, respectively inputting the final fusion features into a first detection head, a second detection head and a third detection head, and respectively carrying out classification and positioning processing to obtain a target detection result. According to the invention, a YOLO11 framework, a C3K2-DF module, an MA module, an FB module and an adaptive filter technology are combined, and traffic sign identification based on YOLO11 is realized.

This invention discloses a personalized traffic sign discrimination method based on federated learning, comprising: a server initializing model parameters and sending the model to a client; the client receiving the initialized model parameters from the server; preprocessing the data; training the model on the client; the server aggregating the model parameters from the client; the client receiving the aggregated model parameters from the server and loading the aggregated model into its local model; and continuing to train the model until the respective iteration count is reached and the model is output. This invention is a method for traffic sign discrimination in intelligent vehicle networks based on a hybrid model of modified terms of distributed methods such as AIDE and DANE, personalized federated learning, and dynamic calibration terms. This model can consider the computing power of each user and its data distribution, and performs traffic sign discrimination with very low performance while ensuring data privacy and security.

The invention discloses a traffic sign sending end and a traffic sign recognition system, and relates to the technical field of traffic control systems. The system comprises a sensor module which is used for collecting real-time information of traffic markers and surrounding environments of the traffic markers; the data processing module is used for processing and analyzing the information acquired by the sensor; the identity generation module is used for generating corresponding identity information according to the traffic sign information processed by the data processing module, the traffic sign information can be collected, processed and transmitted in real time in the use process, automatic identification of traffic signs is achieved, the accuracy and efficiency of identification are improved, and the user experience is improved. According to the system and the method, the latest traffic sign information can be timely obtained by the vehicle, so that a corresponding driving decision can be better made, the system can record and store a large amount of traffic sign information and carry out comprehensive analysis, and valuable data support is provided for urban traffic planning, road design and other aspects.

The application provides a small traffic sign target detection method under complex weather based on an improved YOLOv5, which is used for solving the problem that the detection speed and recognition accuracy of a traffic sign recognition model are unbalanced and small targets and occluded targets are difficult to detect; the steps are as follows: firstly, a data set of traffic sign images is loaded, and the images in the data set are subjected to data enhancement; secondly, an improved YOLOv5 network model is constructed, and model input parameters are set; then, the improved YOLOv5 network model is trained by using the traffic sign image data subjected to data enhancement, so that a detection model is obtained; finally, the detection model is used for predicting an image to be recognized, and the position of the traffic sign and the category to which the traffic sign belongs are output. The application improves the feature extraction capability and detection efficiency by adding a coordinate attention mechanism and a small target detection layer, and further improves the accuracy of small target detection by using an improved loss function.