CN110990658A - Method for realizing image processing algorithm of power transmission line on embedded system - Google Patents
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Abstract
The utility model discloses a method for realizing image processing algorithm of power transmission line on an embedded system, which comprises the following steps: downloading a Linux operating system, programming the Linux operating system into an SD card, and activating a Jetson Nano development board; installing CUDA, OpenCV and CUDNN toolkits; downloading a PyTorch framework and installing the PyTorch framework through a Pip tool box; and transplanting the trained image processing algorithm of the power transmission line to an embedded system. According to the method and the device, the image processing algorithm of the power transmission line is deployed on the embedded equipment with low power consumption and applied to the actual online monitoring device of the power transmission line, so that the data processing time can be shortened, the timeliness of data processing is improved, and the cloud processing pressure is reduced.
Description
Technical Field
The disclosure belongs to the field of image processing, and particularly relates to a method for realizing an image processing algorithm of a power transmission line on an embedded system.
Background
The transmission line is easy to be damaged by the outside because the environment is complicated and changeable and is exposed outside all the year round. As an important component of electric energy transmission, the normal operation of the transmission line is the premise of ensuring the stability of the power grid, and with the continuous development of the power grid in China, the distribution of the transmission line is more and more intensive, the accuracy rate of detection and early warning by means of the traditional image processing technology is lower, and the intelligent construction of the power grid is severely restricted.
Compared with traditional manual inspection, the centralized processing is carried out after image acquisition by utilizing existing monitoring equipment, an unmanned aerial vehicle and the like, although the workload of workers is greatly reduced, the problems of low processing speed, poor timeliness and the like can occur when the acquired massive images and videos are all processed by a cloud end, therefore, the detection method is deployed to the embedded equipment to operate, and the intelligent processing is carried out on the embedded equipment, so that the method is a better practical mode.
The deep learning is taken as the latest research trend in the field of artificial intelligence at present, revolutionary changes are brought to the field of computer vision and image processing, the deep learning and image processing technology are combined and applied to the online monitoring of the power transmission line, and the power transmission line is deployed to an embedded device to run, so that the routing inspection process is more efficient, intelligent and accurate.
Disclosure of Invention
Aiming at the defects in the prior art, the disclosure aims to provide a method for realizing an image processing algorithm of a power transmission line on an embedded system.
In order to achieve the above purpose, the present disclosure provides the following technical solutions:
a method for realizing an image processing algorithm of a power transmission line on an embedded system comprises the following steps:
s100: downloading a Linux operating system, programming the Linux operating system into an SD card, and activating a Jetson Nano development board;
s200: installing CUDA, OpenCV and CUDNN toolkits;
s300: downloading a PyTorch framework and installing the PyTorch framework through a Pip tool box;
s400: and transplanting the trained image processing algorithm of the power transmission line to an embedded system.
Preferably, the power transmission line image processing algorithm is composed of a convolutional neural network, the convolutional neural network comprises a network configuration file, a weight file and a type file, and the network configuration file, the weight file and the type file are placed in the same file folder named in advance.
Preferably, the network configuration file is used for storing a network structure and training and testing parameters of the image processing algorithm of the power transmission line.
Preferably, the weight file is a binary file, and the weights of the convolutional neural network are stored in a sequence manner.
Preferably, the type file is used for storing the hidden danger types of the power transmission line, which can be detected by the power transmission line image processing algorithm.
Preferably, the hidden danger types of the power transmission line comprise any one or more of a crane, a tower crane, construction machinery, a wire foreign body and smoke.
Preferably, after the trained image processing algorithm of the power transmission line is transplanted to an embedded system, a test image needs to be placed in a folder which is the same as the network configuration file, the weight file and the type file, and the test image is input to the convolutional neural network for testing by running a test program.
Preferably, after the test image is tested, the acquired power transmission line image is placed in a folder where the test image is located to be detected in the same mode.
Preferably, before the test image is tested or the collected transmission line image is detected, a virtual memory needs to be added and an image interface needs to be closed.
The present disclosure also provides a method for processing an image of a power transmission line, including the following steps:
s11: collecting an image of the power transmission line through online monitoring equipment;
s12: constructing an image processing algorithm model according to the collected power transmission line image;
s13: implementing the image processing algorithm model into an embedded system using the implementation method of claim 1;
s14: and processing the image of the power transmission line by using the embedded system, and detecting the potential environmental hazards of the power transmission line, including a crane, a tower crane, construction machinery, foreign matters of a wire and smoke.
Compared with the prior art, the beneficial effect that this disclosure brought does: the image processing algorithm of the power transmission line is deployed on the embedded equipment with low power consumption and applied to the actual online monitoring device of the power transmission line, so that the data processing time can be shortened, the timeliness of data processing is improved, and the cloud processing pressure is reduced.
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Fig. 1 is a flowchart of a method for implementing an image processing algorithm for a power transmission line on an embedded system according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for processing an image of a power transmission line according to another embodiment of the present disclosure.
Detailed Description
In the following description, numerous details are set forth to provide a more thorough explanation of embodiments of the present disclosure. It will be apparent, however, to one skilled in the art that embodiments of the invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present disclosure. Furthermore, features of different embodiments described below may be combined with each other, unless specifically stated otherwise.
The terms "including" and "having," and any variations thereof, as used in this disclosure, are intended to cover and not be exhaustive. For example, a process, method, system, or article or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, system, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the disclosure, and the embodiment described is a portion of but not all embodiments of the disclosure. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It will be appreciated by those skilled in the art that the embodiments described herein may be combined with other embodiments.
The technical solution of the present disclosure is described in detail below with reference to fig. 1 to 2 and the embodiments.
Referring to fig. 1, in an embodiment, the present disclosure provides a method for implementing a power transmission line image processing algorithm on an embedded system, including the following steps:
s100: downloading a Linux operating system, programming the Linux operating system into an SD card, and activating a Jetson Nano development board;
s200: installing CUDA, OpenCV and CUDNN toolkits;
s300: downloading a PyTorch framework and installing the PyTorch framework through a Pip tool box;
s400: and transplanting the trained image processing algorithm of the power transmission line to an embedded system.
By the method, the image processing algorithm of the power transmission line can be deployed on the embedded system with low power consumption, compared with the existing method, the data processing time can be shortened, the timeliness of data processing is improved, and therefore the problems of low processing speed and poor timeliness existing in the process of processing the image from the cloud end can be solved.
It should be noted that, in this embodiment, the internal memory of the development board Jetson Nano is 4GB, the downloaded Linux operating system is a Linux ubuntu18.04 system, and CUDA, OpenCV, and CUDNN respectively adopt CUDA10.0, opencv3.3.1, and CUDNN7.3.1.
In another embodiment, the image processing algorithm of the power transmission line is composed of a convolutional neural network, the convolutional neural network comprises a network configuration file, a weight file and a type file, and the network configuration file, the weight file and the type file are placed in the same pre-named folder.
In another embodiment, the network configuration file is used for storing a network structure and training and testing parameters of the image processing algorithm of the power transmission line.
In another embodiment, the weight file is a binary file, and the weights of the convolutional neural network are stored in a sequence manner.
In another embodiment, the type file is used for storing the hidden danger types of the power transmission line, which can be detected by the power transmission line image processing algorithm.
In another embodiment, the hidden danger types of the power transmission line comprise any one or more of a crane, a tower crane, construction machinery, a wire foreign body and smoke.
In another embodiment, after the trained image processing algorithm of the power transmission line is transplanted to an embedded system, a test image needs to be placed in a folder which is the same as the network configuration file, the weight file and the type file, and the test image is input to the convolutional neural network for testing by running a test program.
In another embodiment, after the test image is tested, the collected power transmission line image is placed in a folder where the test image is located to be detected in the same manner.
In another embodiment, because the original method has a memory overflow error, this embodiment proposes a new method written using a PyTorch frame, which is used to process the image of the power transmission line, as shown in fig. 2, and specifically includes the following steps:
s11: collecting an image of the power transmission line through online monitoring equipment;
s12: constructing an image processing algorithm model according to the collected power transmission line image;
in the step, the image processing algorithm model consists of a series of 1 × 1 and 3 × 3 convolutional layers, each convolutional layer is connected with a BN layer for standardization operation, then connected with a Leaky ReLU activation function, a total of 53 convolutional networks are used for carrying out feature extraction on the image of the power transmission line, and then a feature interaction layer is arranged. And carrying out non-maximum suppression algorithm processing on the plurality of predicted boundary boxes to obtain a final boundary box, and converting the final boundary box to an original image to obtain the category and the position of the hidden danger.
After the algorithm model is built, the collected power transmission line images are divided into 5 types including foreign matters of wires, construction machinery, a tower crane, a crane and smoke by using marking software, the collected images are trained by using a pre-trained convolutional neural network, and the network weight coefficient is updated by using a random gradient descent (SGD) method. The initial learning rate is set to 0.001, the momentum coefficient is set to 0.9, and the weighted attenuation coefficient is set to 0.0005. The number of images input by the network at a time is 8. The learning rate is updated by adopting an exponential decay method, the learning rate is multiplied by 0.9 every 5 training rounds, and the training is carried out for 80 times in total, so that the network weight finally used for evaluation is obtained.
S13: implementing the image processing algorithm model into an embedded system according to the steps shown in FIG. 1;
s14: and processing the image of the power transmission line by using the embedded system, and detecting the potential environmental hazards of the power transmission line, including a crane, a tower crane, construction machinery, foreign matters of a wire and smoke.
In the step, the processing of the power transmission line image is mainly realized through Jetson Nano, and the specific method comprises the following steps: performing feature extraction on the acquired power transmission line image by using a neural convolution network in a GPU (graphics processing Unit) acceleration reasoning mode and constructing a feature map; and predicting the boundary boxes of the feature map according to the feature map, and processing the plurality of predicted boundary boxes by using a non-maximum suppression algorithm to obtain the types and positions of the hidden dangers.
The intelligent processing method for the electric transmission line image, which is adopted by the invention, has strong robustness and high detection precision, can carry out detection in a complex environment, and is more convenient to analyze the state of the electric transmission line when being deployed on an embedded device.
The above embodiments are only used to help understanding the core idea of the present invention, and should not be taken as limiting the scope of the present invention; meanwhile, for a person skilled in the art, any changes made in the embodiments and the application range according to the idea of the present invention are considered to be within the protection scope of the present invention.
Claims (10)
1. A method for realizing an image processing algorithm of a power transmission line on an embedded system comprises the following steps:
s100: downloading a Linux operating system, programming the Linux operating system into an SD card, and activating a Jetson Nano development board;
s200: installing CUDA, OpenCV and CUDNN toolkits;
s300: downloading a PyTorch framework and installing the PyTorch framework through a Pip tool box;
s400: and transplanting the trained image processing algorithm of the power transmission line to an embedded system.
2. The method according to claim 1, wherein the image processing algorithm for the power transmission line is preferably composed of a convolutional neural network, the convolutional neural network comprises a network configuration file, a weight file and a type file, and the network configuration file, the weight file and the type file are placed in the same pre-named folder.
3. The method of claim 2, wherein the network profile is used to store network structure and training and testing parameters of the transmission line image processing algorithm.
4. The method of claim 2, wherein the weight file is a binary file, and wherein the weights of the convolutional neural network are stored in a sequence.
5. The method according to claim 2, wherein the type file is used for storing the hidden danger types of the power transmission line, which can be detected by the power transmission line image processing algorithm.
6. The method according to claim 5, wherein the power transmission line hidden danger types comprise any one or more of cranes, tower cranes, construction machinery, foreign wire objects and fireworks.
7. The method according to claim 2, wherein after the trained image processing algorithm for the power transmission line is transplanted to the embedded system, a test image needs to be placed in a folder which is the same as the network configuration file, the weight file and the type file, and the test image is input to the convolutional neural network for testing by running a test program.
8. The method according to claim 7, characterized in that after the test of the test image is completed, the acquired transmission line image is placed in a folder in which the test image is located to perform detection in the same manner.
9. The method according to claim 7 or 8, wherein a virtual memory needs to be added and an image interface needs to be closed before the test image is tested or the acquired transmission line image is detected.
10. A method for processing images of a power transmission line comprises the following steps:
s11: collecting an image of the power transmission line through online monitoring equipment;
s12: constructing an image processing algorithm model according to the collected power transmission line image;
s13: implementing the image processing algorithm model into an embedded system using the implementation method of claim 1;
s14: and processing the image of the power transmission line by using the embedded system, and detecting the potential environmental hazards of the power transmission line, including a crane, a tower crane, construction machinery, foreign matters of a wire and smoke.
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