CN108545021A - A kind of auxiliary driving method and system of identification special objective - Google Patents
A kind of auxiliary driving method and system of identification special objective Download PDFInfo
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- CN108545021A CN108545021A CN201810343108.9A CN201810343108A CN108545021A CN 108545021 A CN108545021 A CN 108545021A CN 201810343108 A CN201810343108 A CN 201810343108A CN 108545021 A CN108545021 A CN 108545021A
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- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000013135 deep learning Methods 0.000 claims abstract description 6
- 238000013136 deep learning model Methods 0.000 claims description 7
- 238000003062 neural network model Methods 0.000 claims description 7
- 241001465754 Metazoa Species 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
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- 238000013527 convolutional neural network Methods 0.000 description 1
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- 208000013409 limited attention Diseases 0.000 description 1
- 210000001747 pupil Anatomy 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
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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
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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
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Abstract
The present invention provides a kind of auxiliary driving method and system of identification special objective, belong to intelligent driving technical field, the present invention is based on the presence that the images steganalysis system of deep learning can identify special objective when driving, and then remind driver, cause driver's note that enhance traffic safety.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to an auxiliary driving method and system for identifying a special target.
Background
Driving safety is always a common concern of society, and how to effectively improve driving safety and reduce the probability of car accidents is always the direction of effort of people in relation to life safety of drivers and pedestrians. During driving, some special targets appearing in the driving direction and at the roadside need special attention of a driver, and some special targets such as old people, animals, special signs and the like are probably ignored due to limited attention of people. The old man can move relatively slowly on crossing the road, needs driver special care, and some driving signs can instruct following road conditions, and these targets all can influence driving safety.
The algorithm of deep learning is mature day by day, especially the image target identification is excellent, the calibrated target can be effectively identified, and the traditional target identification method generally comprises three stages: firstly, some candidate regions are selected on a given image, then the regions are subjected to feature extraction, and finally, a trained classifier is used for classification. Respectively as follows:
a) and (3) area selection: and using sliding windows with different sizes to frame a certain part in the image as a candidate area.
b) Feature extraction: and extracting visual features related to the candidate region. Such as Harr features commonly used for face detection; the HOG features commonly used for pedestrian detection and general target detection, and the like. Due to the factors such as the form diversity, the illumination variation diversity and the background diversity of the target, it is not easy to design a robust feature, but the classification accuracy is directly influenced by the quality of the extracted feature.
c) A classifier: the recognition is performed using a classifier, such as a commonly used SVM model.
The current algorithm capable of realizing image target recognition can effectively realize human recognition of special targets, and only needs to retrain according to different targets.
Disclosure of Invention
In order to solve the technical problem, the invention provides an auxiliary driving method for identifying a special target. Can effectively discern special targets such as tablet, old man, animal to remind the driver to pay attention to through the mode of voice broadcast.
A driving assisting method for recognizing a special target trains a model for recognizing the special target in a driving process based on a deep learning algorithm, applies the trained deep learning model to a driving assisting system, and reminds a driver of the attention in a voice broadcasting mode.
The specific operation steps are as follows:
1) firstly, acquiring actual driving images through a vehicle-mounted camera, selecting a neural network model for image recognition, setting a special target type, and training at a server end according to a calibration data set;
2) the trained deep learning model for recognizing the special target is realized through a special target recognition module of a vehicle-mounted system;
3) the method comprises the following steps of collecting images in real time in the driving process, identifying the type of a special target through a special target identification module, and transmitting the type of the special target to a vehicle-mounted processor;
4) and the vehicle-mounted processor controls the vehicle-mounted sound box to play voice prompt according to the recognized target type, indicates the special target type and advises a driver to take proper operation.
The invention also provides an auxiliary driving system for identifying the special target, which mainly comprises a camera arranged on the roof, a special target identification module, a vehicle-mounted processor and a sound box; wherein,
a camera: collecting a driving image;
a special target identification module: realizing a trained deep learning model for recognizing a special target;
an onboard processor: selecting a neural network model for image recognition, setting a special target type, and training according to a calibration data set; and controlling the vehicle-mounted sound equipment to play voice prompt according to the recognized target type, indicating the special target type and advising the driver to take proper operation.
The invention has the advantages that
The image target recognition in the driving process is realized according to the algorithm design of the deep learning, the special target appearing in the driving direction is effectively recognized, the monitor is reminded in a voice mode, and the effect of enhancing the driving safety is achieved.
Drawings
FIG. 1 is a system block diagram of the present invention;
FIG. 2 is a flow chart of the training of the present invention.
Detailed Description
The invention is explained in more detail below:
the whole system comprises a camera arranged on the roof of the vehicle, a special target identification module, a vehicle-mounted processor and a sound box, and is shown in the attached figure 1. The deep learning target recognition module supports a current common image target recognition model to be trained, for example, a neural network model such as SSD, R-CNN, etc., the process from training to application is shown in fig. 2, and the neural network model only needs to recognize a manually set special target, which includes: animals, pupils, signs, red lights, the elderly, service areas, gas stations; according to the number of special targets to be identified, the number of categories of the final classification layer of the neural network needs to be adjusted.
The specific working process is as follows:
1) firstly, acquiring actual driving images through a vehicle-mounted camera, selecting a neural network model for image recognition, setting a special target type, and training at a server end according to a calibration data set;
2) the trained deep learning model for recognizing the special target is realized through a special target recognition module of a vehicle-mounted system;
3) the method comprises the following steps of collecting images in real time in the driving process, identifying the type of a special target through a special target identification module, and transmitting the type of the special target to a vehicle-mounted processor;
4) and the vehicle-mounted processor controls the vehicle-mounted audio to play voice reminding according to the recognized target type, indicates a special target type and advises a driver to take proper operation, such as chronic operation, parking and the like.
Claims (3)
1. A driving assistance method for recognizing a specific object is characterized in that,
the method comprises the steps of training and recognizing a model of a special target in a driving process based on a deep learning algorithm, applying the trained deep learning model to an auxiliary driving method, and reminding a driver of attention in a voice broadcasting mode.
2. The method of claim 1,
the specific operation steps are as follows:
1) firstly, acquiring actual driving images through a vehicle-mounted camera, selecting a neural network model for image recognition, setting a special target type, and training at a server end according to a calibration data set;
2) the trained deep learning model for recognizing the special target is realized through a special target recognition module of a vehicle-mounted system;
3) the method comprises the following steps of collecting images in real time in the driving process, identifying the type of a special target through a special target identification module, and transmitting the type of the special target to a vehicle-mounted processor;
4) and the vehicle-mounted processor controls the vehicle-mounted sound box to play voice prompt according to the recognized target type, indicates the special target type and advises a driver to take proper operation.
3. An auxiliary driving system for identifying a special target is characterized by mainly comprising a camera arranged on the roof of a vehicle, a special target identification module, a vehicle-mounted processor and a sound box; wherein,
a camera: collecting a driving image;
a special target identification module: realizing a trained deep learning model for recognizing a special target;
an onboard processor: selecting a neural network model for image recognition, setting a special target type, and training according to a calibration data set; and controlling the vehicle-mounted sound equipment to play voice prompt according to the recognized target type, indicating the special target type and advising the driver to take proper operation.
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| CN201810343108.9A CN108545021A (en) | 2018-04-17 | 2018-04-17 | A kind of auxiliary driving method and system of identification special objective |
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| CN201810343108.9A CN108545021A (en) | 2018-04-17 | 2018-04-17 | A kind of auxiliary driving method and system of identification special objective |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110796883A (en) * | 2019-11-06 | 2020-02-14 | 山东浪潮人工智能研究院有限公司 | Electric bicycle violation reminding method and device based on image recognition |
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