CN111178153A - Traffic sign detection method and system - Google Patents
Traffic sign detection method and system Download PDFInfo
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- CN111178153A CN111178153A CN201911253896.3A CN201911253896A CN111178153A CN 111178153 A CN111178153 A CN 111178153A CN 201911253896 A CN201911253896 A CN 201911253896A CN 111178153 A CN111178153 A CN 111178153A
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- 238000013527 convolutional neural network Methods 0.000 claims abstract description 55
- 238000000605 extraction Methods 0.000 claims abstract description 28
- 238000012549 training Methods 0.000 claims description 34
- 238000000034 method Methods 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 8
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/582—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of traffic signs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
Abstract
The embodiment of the invention provides a traffic sign detection method and system, firstly, acquiring a to-be-detected image corresponding to a traffic sign; then, inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected; wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification. Compared with the prior art, the scheme can greatly improve the detection effect of small targets such as traffic signs in target detection.
Description
Technical Field
The invention relates to the field of artificial intelligence and computer vision, in particular to a traffic sign detection method and system.
Background
In recent years, the automatic driving technology is receiving more and more attention and becomes one of the important development directions of future automobiles, and the rapid development of machine learning (such as deep learning) also provides a new solution for various problems related to automatic driving. The rapid and accurate detection of traffic signs is a necessary capability for automatically driving vehicles, and the currently common methods mainly fall into two categories: conventional image processing algorithms and deep learning algorithms.
The traditional image algorithm: the traditional image algorithm has been studied for a long time, and the traffic sign is generally recognized by extracting the characteristics of the shape, the color, the texture and the like of the image. Therefore, such algorithms need to be designed starting from the characteristics of traffic signs. The disadvantage is that the traffic signs in the real environment are affected by various factors such as illumination, shadow, aging, fading and the like, and are often greatly different. On one hand, the traditional algorithm cannot cover all features, and on the other hand, targets of non-traffic signs are easily mistaken for traffic signs, so that the recognition rate is difficult to improve.
And (3) deep learning algorithm: based on the idea of convolutional neural network, many excellent algorithms such as RCNN series, SSD and YOLO series have emerged in the target detection field in recent years. These deep learning algorithms exhibit very fast speed and high accuracy in the detection of many objects (e.g., people, cars, animals, etc.).
However, in the above mainstream objects, the occupied space of the traffic sign in one picture is relatively much smaller, which also results in that the recall rate of the mainstream algorithms during the detection of the traffic sign is low, that is, the missed detection is serious.
Disclosure of Invention
Embodiments of the present invention provide a method and system for interprocess communication based on a publish-subscribe pattern, which overcome the above problems or at least partially solve the above problems.
In a first aspect, an embodiment of the present invention provides a traffic sign detection method, including:
acquiring an image to be detected corresponding to a traffic sign;
inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected;
wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification.
Optionally, the detection layer is configured to perform feature extraction on the image to be detected to obtain a medium-sized feature, a small-sized feature, or a micro-sized feature of the image to be detected.
Optionally, the inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected includes:
performing feature extraction on the image to be detected by using the detection layer to obtain a detection result of the image to be detected;
and classifying the detection result by utilizing the classification layer to obtain the classification result of the traffic sign in the image to be detected.
Optionally, the method further comprises:
acquiring a training data set;
and training the initial convolutional neural network by using the training data set to obtain the trained convolutional neural network.
Optionally, the training the initial convolutional neural network by using the training data set to obtain the trained convolutional neural network specifically includes:
and firstly, training a detection layer in the initial convolutional neural network by using the training data set, and then training a classification layer in the initial convolutional neural network to obtain the trained convolutional neural network.
In a second aspect, an embodiment of the present invention provides a traffic sign detection system, including:
the image acquisition module is used for acquiring an image to be detected corresponding to the traffic sign;
the image detection module is used for inputting the image to be detected into the trained convolutional neural network and outputting the classification result of the traffic sign in the image to be detected;
wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification.
Optionally, the detection layer is configured to perform feature extraction on the image to be detected to obtain a medium-sized feature, a small-sized feature, or a micro-sized feature of the image to be detected.
Optionally, the image detection module is specifically configured to:
performing feature extraction on the image to be detected by using the detection layer to obtain a detection result of the image to be detected;
and classifying the detection result by utilizing the classification layer to obtain the classification result of the traffic sign in the image to be detected.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the traffic sign detection method according to the first aspect are implemented.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of traffic sign detection as provided in the first aspect.
The embodiment of the invention provides a traffic sign detection method and system, firstly, acquiring a to-be-detected image corresponding to a traffic sign; then, inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected; wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification. Compared with the prior art, the scheme can greatly improve the detection effect of small targets such as traffic signs in target detection.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a flowchart of a traffic sign detection method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a particular implementation of a traffic sign detection method in an embodiment of the invention;
fig. 3 is a block diagram of a traffic sign detection system according to an embodiment of the present invention;
fig. 4 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a traffic sign detection method according to an embodiment of the present invention, and as shown in fig. 1, the method includes:
s101, acquiring an image to be detected corresponding to a traffic sign;
s102, inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected;
wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification.
Specifically, aiming at the problems existing in the prior art, the embodiment of the invention realizes the traffic sign detection method through the newly designed convolutional neural network, and the neural network can be divided into two parts: a detection layer and a classification layer. The detection layer is a convolution layer comprising a plurality of convolution kernels, and each convolution layer is used for extracting different features in the image to be detected. And after the feature extraction is finished, classifying by utilizing the classification layer based on the extracted features to obtain a classification result of the traffic sign in the image to be detected, thereby finishing the detection of the traffic sign.
The embodiment of the invention provides a traffic sign detection method, which comprises the steps of firstly, acquiring an image to be detected corresponding to a traffic sign; then, inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected; wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification. Compared with the prior art, the scheme can greatly improve the detection effect of small targets such as traffic signs in target detection.
In an optional embodiment of the present invention, the detection layer is configured to perform feature extraction on the image to be detected, so as to obtain a medium-sized feature, a small-sized feature, or a micro-sized feature of the image to be detected.
The image to be detected corresponding to the traffic sign further includes medium-sized features, small-sized features or micro-sized features, so that the scheme focuses more on the features.
In an optional embodiment of the present invention, the inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of a traffic sign in the image to be detected includes:
performing feature extraction on the image to be detected by using the detection layer to obtain a detection result of the image to be detected;
and classifying the detection result by utilizing the classification layer to obtain the classification result of the traffic sign in the image to be detected.
Specifically, in the embodiment of the present invention, the detection process of the traffic sign is performed in the detection layer and the classification layer, which specifically includes the following processes
(1) Feature extraction in detection layer
In the existing feature extraction network, generally, three scale objects are respectively used for detecting objects with large, medium and small sizes in a picture. The embodiment of the invention also designs a feature extraction network with three dimensions, except for the three dimensions of medium, small and tiny.
The detection layer is used for detecting (positioning) the traffic signs in the pictures and has no classification function.
(2) Sorting in a sorting layer
The embodiment of the invention mainly comprises the steps of detecting (positioning) and classifying. It is well known that for any deep learning algorithm, the size of the data set directly relates to the model performance. Traffic signs are of a large number of categories and if the location and classification is done directly together, there is little data for each category. This results in poor positioning. All kinds of traffic signs are classified into a training detection layer, which is beneficial to improving the positioning accuracy.
The classification layer is used for classifying the traffic signs detected by the detection layer, as shown in fig. 2, and the following flow chart inputs the pictures into the trained network to perform the process of locating and classifying the traffic signs.
In an optional embodiment of the invention, the method further comprises:
acquiring a training data set;
and training the initial convolutional neural network by using the training data set to obtain the trained convolutional neural network.
In an optional embodiment of the present invention, the training an initial convolutional neural network with the training data set to obtain the trained convolutional neural network specifically includes:
and firstly, training a detection layer in the initial convolutional neural network by using the training data set, and then training a classification layer in the initial convolutional neural network to obtain the trained convolutional neural network.
Fig. 3 is a block diagram of a traffic sign detection system according to an embodiment of the present invention, and as shown in fig. 3, the system includes: an image acquisition module 301 and an image detection module 302. Wherein the content of the first and second substances,
the image acquisition module 301 is configured to acquire an image to be detected corresponding to a traffic sign; the image detection module 302 is configured to input the image to be detected into the trained convolutional neural network, and output a classification result of the traffic sign in the image to be detected; wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification.
The embodiment of the invention provides a traffic sign detection system, which comprises the steps of firstly, acquiring an image to be detected corresponding to a traffic sign; then, inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected; wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification. Compared with the prior art, the scheme can greatly improve the detection effect of small targets such as traffic signs in target detection.
Further, the detection layer is used for performing feature extraction on the image to be detected to obtain a medium-sized feature, a small-sized feature or a micro-sized feature of the image to be detected.
Further, the image detection module is specifically configured to:
performing feature extraction on the image to be detected by using the detection layer to obtain a detection result of the image to be detected;
and classifying the detection result by utilizing the classification layer to obtain the classification result of the traffic sign in the image to be detected.
Further, the system further comprises:
the training data set acquisition module is used for acquiring a training data set;
and the training module is used for training the initial convolutional neural network by utilizing the training data set to obtain the trained convolutional neural network.
Further, the training module is specifically configured to:
and firstly, training a detection layer in the initial convolutional neural network by using the training data set, and then training a classification layer in the initial convolutional neural network to obtain the trained convolutional neural network.
Fig. 4 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device may include: a processor (processor)410, a communication Interface 420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke a computer program stored on the memory 430 and executable on the processor 410 to perform the network topology detection methods provided by the above-described method embodiments, including, for example: acquiring an image to be detected corresponding to a traffic sign; inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected; wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the network topology detection method provided in the foregoing method embodiments, and for example, the method includes: acquiring an image to be detected corresponding to a traffic sign; inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected; wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A traffic sign detection method, comprising:
acquiring an image to be detected corresponding to a traffic sign;
inputting the image to be detected into a trained convolutional neural network, and outputting a classification result of the traffic sign in the image to be detected;
wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification.
2. The method according to claim 1, wherein the detection layer is used for performing feature extraction on the image to be detected to obtain a medium-sized feature, a small-sized feature or a micro-sized feature of the image to be detected.
3. The method according to claim 2, wherein the inputting the image to be detected into a trained convolutional neural network and outputting the classification result of the traffic sign in the image to be detected comprises:
performing feature extraction on the image to be detected by using the detection layer to obtain a detection result of the image to be detected;
and classifying the detection result by utilizing the classification layer to obtain the classification result of the traffic sign in the image to be detected.
4. The method of claim 2, further comprising:
acquiring a training data set;
and training the initial convolutional neural network by using the training data set to obtain the trained convolutional neural network.
5. The method according to claim 4, wherein the training an initial convolutional neural network with the training data set to obtain the trained convolutional neural network specifically comprises:
and firstly, training a detection layer in the initial convolutional neural network by using the training data set, and then training a classification layer in the initial convolutional neural network to obtain the trained convolutional neural network.
6. A traffic sign detection system, comprising:
the image acquisition module is used for acquiring an image to be detected corresponding to the traffic sign;
the image detection module is used for inputting the image to be detected into the trained convolutional neural network and outputting the classification result of the traffic sign in the image to be detected;
wherein, the convolutional neural network comprises a detection layer for feature extraction and a classification layer for classification.
7. The system according to claim 6, wherein the detection layer is configured to perform feature extraction on the image to be detected to obtain a medium-sized feature, a small-sized feature or a micro-sized feature of the image to be detected.
8. The system of claim 7, wherein the image detection module is specifically configured to:
performing feature extraction on the image to be detected by using the detection layer to obtain a detection result of the image to be detected;
and classifying the detection result by utilizing the classification layer to obtain the classification result of the traffic sign in the image to be detected.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the traffic sign detection method according to any of claims 1 to 5 are implemented when the processor executes the program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the traffic sign detection method according to any one of claims 1 to 5.
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