281 results about "Traffic sign recognition" patented technology

This invention provides a vehicle-borne system and method for traffic sign recognition that provides greater accuracy and efficiency in the location and classification of various types of traffic signs by employing rotation and scale-invariant (RSI)-based geometric pattern-matching on candidate traffic signs acquired by a vehicle-mounted forward-looking camera and applying one or more discrimination processes to the recognized sign candidates from the pattern-matching process to increase or decrease the confidence of the recognition. These discrimination processes include discrimination based upon sign color versus model sign color arrangements, discrimination based upon the pose of the sign candidate versus vehicle location and/or changes in the pose between image frames, and/or discrimination of the sign candidate versus stored models of fascia characteristics. The sign candidates that pass with high confidence are classified based upon the associated model data and the drive/vehicle is informed of their presence. In an illustrative embodiment, a preprocess step converts a color image of the sign candidates into a grayscale image in which the contrast between sign colors is appropriate enhanced to assist the pattern-matching process.

A traffic sign recognition system including a detection mechanism adapted for detecting a candidate traffic sign and a recognition mechanism adapted for recognizing the candidate traffic sign as being an electronic traffic sign. A partitioning mechanism may be adapted for partitioning the image frames into a first partition and a second partition. The detection mechanism may use the first portion of the image frames and the recognition mechanism may use the second portion of the image frames. When the candidate traffic sign is detected as an electronic traffic sign, the recognition mechanism may use both the first partition of the image frames and the second portion of the image frames.

The invention discloses a traffic sign deep learning mode identification method. The method includes the following steps: pretreatment is performed on a test sample and, a training sample of a trafficsign image, a residual deep learning network based on the multiscale characteristic weight operation fusion of a convolution nerve network is designed, the characteristic is automatically extracted by the network in order to eliminate the artificial trace mainly through training, and identification is performed on the traffic sign test sample by using a deep color characteristic trained classifier. The image characteristic weight operation combining with the multiscale convolution fusion network is applied to the traffic mode identification technology, the training efficiency of the network is substantially improved, and the problem that the accuracy and the real-time performance are not ideal, the network structure is complex, the training time is long, the stability and the robustness are bad encountered in the traffic sign identification method can be effectively solved. The image recognition accuracy of the trained network in 43 kinds of traffic signs reaches 97%.

The invention provides a positioning method and system based on a safe driving map and binocular recognition of traffic signs. Primary positioning of a running vehicle is carried out via the positioning system by means of a high-precision map. Meanwhile, images of the part in front of the vehicle are acquired, and traffic signs in the images are detected and identified. Coordinates of the traffic signs are recognized and obtained in the high-precision map, the distance between the vehicle and the signs are measured, and the position of the vehicle is calculated by comparing the coordinates of the traffic signs. In this way, the vehicle is positioned. By means of the invention, on the basis of traditional navigation data, acquisition of road traffic signs is added, roads signs are adopted to help positioning vehicles, the distance between vehicles and traffic signs is measured according to coordinates and sizes of signs which are recognized by left and right cameras, and vehicle positions are calculated according to spatial coordinates of traffic signs which are already stored in the high-precision map, so that submeter-level coordinate positioning is provided and a lane-based topology network is established.

The invention belongs to the technical field of computer applying and the subfield of the machine learning theories and application and focuses on the traffic sign recognizing problem in the intelligent traffic technology. A traffic sign recognizing method based on multi-resolution convolution neural networks is characterized by solving the problem that the speed is low when the convolution neural network is used for recognizing traffic signs, two-dimensional images with different resolutions are used as input, the two convolution neural networks with the same structure are operated in parallel to carry out feature mapping and extracting, and accurate classifying and recognizing are carried out based on a weight threshold value trained by the networks. The two CNNs with different resolution branches are used for replacing a basic CNN structure, the overall and outline features can be mapped through the high-resolution image input, the local and detailed features can be mapped through the low-resolution images, the recognizing resolution is guaranteed, and the model training speed is increased.

The invention provides a road traffic sign automatic identification and management system based on deep learning, wherein the system is used for the field of environment perception technology of a smart vehicle. The system comprises a traffic sign acquisition module, a traffic sign identification module and a traffic sign management module. The traffic sign acquisition module acquires a video which comprises the traffic sign and performs matching on each image frame and latitude-and-longitude of equipment in acquiring the image. The traffic sign identification module performs functions of processing the input image, performing positioning detection on the traffic sign, acquiring a candidate area which comprises the traffic sign, and performing classification identification on the traffic sign. The traffic sign management module transmits traffic sign and latitude-and-longitude information to a traffic management department, thereby determining whether placement of the traffic sign is reasonable and performing corresponding adjustment. The road traffic sign automatic identification and management system has advantages of large number of kinds of identified traffic signs, high precision, high real-time performance, etc. The road traffic sign automatic identification and management system further has beneficial effects of reducing effect of factors such as illumination change to image identification, improving interference resistance, and obtaining high identification accuracy and low error identification rate.

The invention provides a traffic sign identifying method and a traffic sign identifying system based on cascading deep learning. By introducing a cascading convolutional neural network idea, expanding target sign sample space, and adding more samples having supervision functions, identification of traffic signs is additionally provided with more apriori information, and then sample space used for training of an identification device has the higher supervision function. The traffic sign identifying method is advantageous in that by fully using the various characteristic information of the signs, the deficiency of the conventional traffic sign identification based on the neural networks is remedied, and therefore the detection rate and the identification rate of the signs are improved.

The invention discloses a road surface traffic sign recognition method based on a convolution neural network, and the method comprises the following steps: image collection and preprocessing; and convolution neural network structure design and training. The method employs a V-parallax method to obtain a road surface area from an original image, can reduce the impact caused by non-road-surface interference, and improves the extraction precision of a road surface area. The invention employs a vertical view to reconstruct the road surface area, enables unparallel lines, presented in a visual image because of a view angle, to be reconstructed into approximately parallel lines, facilitates the recognition of a road surface traffic sign, and improves the capability of adapting to view angle inclination. The method the deep learning method (convolution neural network), can extract recessive characters reflecting the data essence from a large number of training samples. Compared with a shallow-layer learning classifier, the method is higher in learning efficiency and recognition precision.

The embodiment of the invention provides a method and device for identifying traffic signs. The method comprises the steps that according to collected original color images of the traffic signs, gray level information images and color information images of the original color images are obtained; the color information images are filtered through the gray level information images, so that sign shape images are obtained; shape identification is conducted on the sign shape images, so that areas of interest in the sign shape images are obtained; based on a sample template, the areas of interest are classified and the corresponding traffic signs are determined according to classification results. According to the method and device for identifying the traffic signs, influence on real photos by the lighting condition is avoided and the identification rate of the traffic signs is improved.

The invention discloses an automobile control system based on traffic light recognition, which comprises a controller and a video camera connected with the controller. The controller comprises a communication module, an image processing module and an information processing module connected with the communication module and the image processing module. The invention further discloses a control method corresponding to the control system. The control method includes: acquiring images of road conditions before a vehicle, extracting characteristic information in the images; and performing corresponding judgment according to the characteristic information of the images and operating information of the vehicle. Corresponding feasible voice prompts are provided for a driver in judgment of passing through crossings, and accuracy of driving judgment of the driver can be improved greatly. Further, even the driver makes false judgment and insists on passing through the crossings, the automobile control system can automatically control the vehicle to decelerate and brake, automobile collision accidents can be avoided, and physical safety of drivers and other people can be guaranteed.

The embodiment of the invention discloses a traffic sign recognition test method and device. The traffic sign recognition test method comprises the following steps: in a running process of an unmanned vehicle, receiving identity information corresponding to a traffic marker placed on a running route, and storing the identity information; recording the image recognition information of the unmanned vehicle passing through the traffic maker on the running route obtained by adopting an image recognition technology; and comparing the image recognition information with the identify information to verify recognition accuracy of the unmanned vehicle on the image of the traffic maker. By adopting the technical scheme, the technical effect that recognition accuracy of an image recognition technology for the unmanned vehicle in a technical innovation stage is verified by virtue of the identify information, received by a mature wireless information receiving technology, of the traffic marker is realized, and a verification method is simple and rapid and high in accuracy.

A method for setting up a traffic object recognition system. A scene generator simulates three-dimensional simulations of various traffic situations which include at least one of the traffic objects. A projection unit generates signals which correspond to signals that the sensor would detect in a traffic situation simulated by the three-dimensional simulation. The signals are sent to the evaluation unit for recognizing traffic objects, and the pattern recognition is trained based on a deviation between the traffic objects simulated in the three-dimensional simulations of traffic situations and the traffic objects recognized therein.

The invention discloses a traffic sign detection and recognition method based on a convolutional neural network, which belongs to the field of digital image processing and machine learning. The method comprises steps: firstly, an RGB image after pre-processing is converted to HSV color space, and a region of interest is obtained through threshold setting; and then, a two-classification convolutional neural network for distinguishing a traffic sign and a non-traffic sign is designed to judge whether the region of interest is a traffic sign. After the position of a traffic sign is obtained, the traffic sign recognition method based on the convolutional neural network is used, parameters such as the layer number and the characteristic pattern number of the convolutional neural network are adjusted, parameters in the network are learnt through a large amount of training samples, and classes of traffic signs at different positions are further recognized. An experiment shows that the method has good adaptability to deformation, partial occlusion and tilt and the like of the traffic sign, and good performance is presented in aspects of recognition effects and recognition efficiency.

The present invention relates to a traffic sign recognition device configured to be capable of combining road information received from a navigation terminal with road information acquired through a camera so as to recognize and output a speed limit of a traffic sign to be suitable for a fluid road environment and travel environment on a road, and a method of outputting a speed limit using the traffic sign recognition device.

The invention discloses a traffic sign detection method in automatic driving based on a YOLOv3 network, and belongs to the field of traffic sign detection. The method solves the problems that an existing YOLOv3 network target detection algorithm is not high in detection precision and the detection speed cannot meet the real-time requirement. According to the invention, an improved loss function isprovided, so that the influence of a large target error on a small target detection effect is reduced, and the detection accuracy of a small-size target is improved. An improved activation function is provided, a negative value is reserved, meanwhile, changes and information propagated to the next layer are reduced, and the robustness of the algorithm to noise is enhanced. The real frames in thetraffic sign data set are clustered by using a K-means algorithm to realize the pre-fetching of a target frame position and accelerate convergence of the network. The detection precision mAP of the traffic sign detection model on a test set reaches 92.88%, the detection speed reaches 35FPS, and the requirement for real-time performance is completely met. The method can be applied to the field of traffic sign detection.

The invention relates to the technical field of image identification, in particular to a traffic sign identification method and system based on a SURF. The method includes the following steps that according to a hue value of an image, a traffic sign candidate zone of the image is abstracted and the color of the traffic sign candidate zone is obtained; the shape of the traffic sign candidate zone is detected and the type of a traffic sign is obtained according to the shape and the color of the traffic sign candidate zone; the SURF feature value of the traffic sign is abstracted and described; the SURF feature value matched with the traffic sign is searched for in a preset template feature vector quantity sub-library and the traffic sign in the image is identified. By means of the traffic sign identification method based on the SURF, the efficiency of identifying traffic signs is improved.

The invention discloses a device for processing pillar A blind zones and automatically identifying road conditions. The device can display the road conditions of the pillar A blind zones stereoscopically with a controlled visual angle, and provide the road conditions and a safe driving path for a driver according to road identification and traffic sign identifying information, and comprises blind zone cameras, a pavement camera, a traffic sign camera and displays, wherein each blind zone camera is provided with a visual angle controller; both the visual angle controllers and the blind zone cameras are mounted on the outer sides of a left pillar A and a ring pillar A; the circular arc-shaped displays are mounted on the inner sides of the pillars A; and the blind zone cameras are connected to the displays through video processors. The device further comprises the processors and a ranging sensor, wherein the processors are connected with the visual angle controllers and the ranging sensor, and connected to a communication bus on a vehicle.

Disclosed are an apparatus for recognizing a traffic sign and an operating method thereof. The apparatus for recognizing a traffic sign according to an exemplary embodiment of the present invention includes: an image obtaining unit configured to obtain an image from a vehicle to a predetermined range; a region of interest designating unit configured to recognize a traffic sign within the image, and designate an area including the traffic sign as a region of interest; a valid area extracting unit configured to extract a valid area except for an area of a first color from the region of interest, and calculate valid area data; and a similarity calculating unit configured to calculate similarity of the traffic sign by using the valid area data.

The invention relates to a method and a device for processing traffic sign symbols; the method comprises the steps: acquiring obtained streetscape images; generating candidate windows of non-blue traffic sign symbols of the images and the candidate windows of blue traffic sign symbols; classifying and identifying the generated candidate windows by adopting a preset classifier; and judging whether the candidate windows have traffic signs and traffic sign types in the generated candidate windows according to the classification and identification result. According to the method and the device for processing the traffic sign symbols, the traffic signs can be identified rapidly in the images corresponding to complex scenes, the time-consumed sliding of the windows is avoided, the traffic sign identification efficiency is improved and the manpower resource and time cost are also reduced.

The invention relates to a traffic sign identification method and a device thereof. The traffic sign identification method adopts relative variables of three components in an RGB space as new variables for dividing a color region; the new relation of corresponding variables in the RGB and the HIS spaces is obtained by the variables and the expression of color tone in the transformation formula of the universal RGB-HIS spaces, and a first threshold value scope in the color region is divided according to a color tone variable H in the HSI space; and a second threshold value scope of the relative variables in the corresponding RGB space is obtained, thereby abstracting the color of an image to achieve to obtain the interested region of the traffic sign according to the value of the relative variables obtained from the image. The traffic sign identification method and the device thereof have good real time, as well as higher identification efficient and accuracy.

The invention aims to solve the problems in the existing traffic signboard recognition method that the recognition target falls into a single group and the speed in doing so is slow. The invention, out of this concept, provides a fast traffic signboard recognition method based on convolution neural network, referred to as FTSR-CNN in abbreviation. This method comprises: using the convolution kernel sliding filter extracted characteristics; obtaining the loss of the network in the forward learning process, and ensuring the accuracy of the network model to the recognition of multiple categories of signboards; optimizing the network performances through the adjustment of the parameters, the activation of the function types and the reduction of dimensions for better accuracy and timeliness eventually; and at the same time, in order to make the samples more diverse, conducting data adding and expanding to the samples in the data set based on affine transformation. The recognition rates of the FTSR-CNN for two data set tests of the German traffic signboard data set GTSRB and the Tsinghua-Tencent 100K are recorded as 95.74% and 96.67% respectively. The results indicate that the recognition speed is increased on the same recognition accuracy level through the modification of a previous model network and the start up of different training strategies by the FTSR-CNN.