CN114627388A - Power transmission line foreign matter detection equipment and foreign matter detection method thereof - Google Patents
Power transmission line foreign matter detection equipment and foreign matter detection method thereof Download PDFInfo
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Abstract
The utility model relates to a transmission line foreign matter detection equipment and foreign matter detection method thereof, transmission line foreign matter detection equipment includes image acquisition device, treater and cloud end server, image acquisition device connects the treater, the treater communicates with the cloud end server, image acquisition device and treater all set up in transmission line, image acquisition device is used for gathering transmission line's state image and sending to the treater, the treater is used for carrying out the target detection to the state image, after detecting there is the state image that has the foreign matter, send processing signal to the cloud end server, the cloud end server is used for judging whether to issue according to processing signal and reports an emergency and asks for help or increased vigilance. Whether foreign matters exist on the primary power transmission line is recognized through the image acquisition device and the processor, the number of images sent to the cloud server can be reduced, dependence on a high-reliability network is reduced, network expenses are saved, energy consumption is reduced, and the use is reliable.
Description
Technical Field
The application relates to the technical field of power grid maintenance, in particular to a power transmission line foreign matter detection device and a foreign matter detection method thereof.
Background
With the development of electric power industry in China and the implementation of strategies such as western electricity and east electricity transmission, the safe operation of the power transmission line is one of important links for guaranteeing the stability of a power grid. However, the invasion of foreign matters into the wires is a phenomenon commonly existing in the power transmission line, and the easily floating objects such as kites, reflective films, dust screens and the like become a great hidden danger threatening the stable operation of the high-voltage power transmission line. Therefore, the invasion of the foreign matters in the power transmission line needs to be monitored, effective early warning is realized when the foreign matters are hung on the wires, and measures are taken to remove the foreign matters hung on the wires.
The traditional method for monitoring the foreign matter invasion of the power transmission line is based on camera monitoring, the state of the power transmission line is shot through a camera arranged on a power transmission tower or other positions, and a picture is transmitted to a server through a mobile network to be subjected to foreign matter detection. However, the method needs to continuously upload the pictures to the cloud server, has high requirements on the network state, is easy to cause unstable transmission, has high network communication cost and high use cost, is not suitable for long-term use, and is unreliable in work.
Disclosure of Invention
In view of the above, it is necessary to provide a power transmission line foreign object detection apparatus and a foreign object detection method thereof, which have low energy consumption and reliable use.
In a first aspect, the application provides a power transmission line foreign matter detection device, which comprises an image acquisition device, a processor and a cloud server, wherein the image acquisition device is connected with the processor, the processor is communicated with the cloud server, and the image acquisition device and the processor are both arranged on a power transmission line;
the image acquisition device is used for acquiring the state image of the power transmission line and sending the state image to the processor, the processor is used for carrying out target detection on the state image, and sending a processing signal to the cloud server after the state image with foreign matters is detected, and the cloud server is used for judging whether to issue an alarm according to the processing signal.
In one embodiment, the processor comprises a microcontroller unit and an embedded neural network processing unit, the image acquisition device and the embedded neural network processing unit are both connected with the microcontroller unit, and the microcontroller unit is communicated with the cloud server;
the microcontroller unit is used for receiving the state image and transmitting the state image to the embedded neural network processing unit, the embedded neural network processing unit carries out target detection on the state image, and after the state image with foreign matters is detected, a processing signal is sent to the cloud server through the microcontroller unit.
In one embodiment, the image acquisition device comprises an RGB camera and an infrared camera, and both the RGB camera and the infrared camera are connected to the processor.
In one embodiment, the processor is configured to segment the state image from the RGB camera and perform target detection on the segmented sub-images, respectively.
In one embodiment, the processor is configured to segment the status image from the RGB camera using a bisection or octagon method.
In one embodiment, the processor is configured to perform target detection on the status image based on a first weight, and the cloud server is configured to perform target review on the received status image with the existence of the foreign object based on a second weight, wherein parameters of the first weight are less than parameters of the second weight.
In a second aspect, the application further provides a foreign object detection method for the power transmission line foreign object detection equipment, the power transmission line foreign object detection equipment comprises an image acquisition device, a processor and a cloud server, the image acquisition device is connected with the processor, the processor is communicated with the cloud server, and the image acquisition device and the processor are both arranged on the power transmission line;
the foreign matter detection method of the power transmission line foreign matter detection equipment is applied to the processor, and the method comprises the following steps:
acquiring a state image of the power transmission line;
carrying out target detection on the state image;
and after the state image with the foreign matter is detected, sending a processing signal to the cloud server, so that the cloud server judges whether to issue an alarm or not according to the processing signal.
In one embodiment, the performing target detection on the state image includes:
and after the state image is segmented, respectively carrying out target detection on the segmented sub-images.
In a third aspect, the application further provides a foreign object detection method for the foreign object detection equipment for the power transmission line, the foreign object detection equipment for the power transmission line comprises an image acquisition device, a processor and a cloud server, the image acquisition device is connected with the processor, the processor is communicated with the cloud server, and the image acquisition device and the processor are both arranged on the power transmission line;
the foreign matter detection method of the power transmission line foreign matter detection device is applied to the cloud server, and the method comprises the following steps:
receiving a processing signal; the processing signal is used for carrying out target detection on a state image by the processor, the state image with foreign matters is sent after being detected, and the state image is acquired by the power transmission line through the image acquisition device;
and judging whether to issue an alarm or not according to the processing signal.
In one embodiment, the image capturing device includes an RGB camera and an infrared camera, and the determining whether to issue an alarm according to the processing signal includes:
and issuing an alarm when the processing signal comprises a first detection result obtained after the processor performs target detection on the state image from the RGB camera and a second detection result obtained after the processor performs target detection on the state image from the infrared camera.
The power transmission line foreign matter detection device comprises an image acquisition device, a processor and a cloud server, wherein the image acquisition device is connected with the processor, the processor is communicated with the cloud server, the image acquisition device and the processor are both arranged on the power transmission line, the image acquisition device is used for acquiring a state image of the power transmission line and sending the state image to the processor, the processor is used for carrying out target detection on the state image, after the state image with the foreign matter is detected, a processing signal is sent to the cloud server, and the cloud server is used for judging whether to issue an alarm or not according to the processing signal. Whether foreign matters exist on the primary power transmission line is recognized through the image acquisition device and the processor, if so, the processing signal is sent to the cloud server, the cloud server judges whether to issue an alarm according to the processing signal, the number of images sent to the cloud server can be reduced, dependence on a high-reliability network is reduced, network expenses are saved, energy consumption is reduced, and the use is reliable.
Drawings
Fig. 1 is a block diagram of a power transmission line foreign matter detection apparatus according to an embodiment;
FIG. 2 is a schematic diagram of bisecting an image in one embodiment;
FIG. 3 is a diagram illustrating an eighth-scoring of an image in one embodiment;
FIG. 4 is a flowchart illustrating the operation of the device for detecting a foreign object in a power transmission line according to an embodiment;
FIG. 5 is a flow diagram of weight training in one embodiment;
FIG. 6 is a flowchart of a method for detecting alien materials in an apparatus for detecting alien materials on an electric power transmission line according to an embodiment;
fig. 7 is a flowchart of a foreign object detection method of the power transmission line foreign object detection apparatus in another embodiment;
fig. 8 is a flowchart of a foreign object detection method of the power transmission line foreign object detection apparatus in still another embodiment;
fig. 9 is a flowchart of a foreign object detection method of the power transmission line foreign object detection apparatus in still another embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, a device for detecting foreign objects on a power transmission line is provided, which is used for detecting the power transmission line and judging whether foreign objects invade the power transmission line. The foreign matter is different from the power transmission line and may affect the normal operation of the power transmission line. Such as kites, Kongming lantern, reflective film, dust screen, etc. Referring to fig. 1, the power transmission line foreign object detection apparatus includes an image acquisition device 300, a processor 100 and a cloud server 200, the image acquisition device 300 is connected to the processor 100, the processor 100 is in communication with the cloud server 200, the image acquisition device 300 and the processor 100 are both disposed in a power transmission line, the image acquisition device 300 is configured to acquire a state image of the power transmission line and send the state image to the processor 100, the processor 100 is configured to perform target detection on the state image, and send a processing signal to the cloud server 200 after detecting that the state image with a foreign object exists, and the cloud server 200 is configured to determine whether to issue an alarm according to the processing signal. Whether foreign matters exist on the primary power transmission line is firstly identified through the image acquisition device 300 and the processor 100, if so, a processing signal is sent to the cloud server 200, the cloud server 200 judges whether to issue an alarm according to the processing signal, the number of images sent to the cloud server 200 can be reduced, dependence on a high-reliability network is reduced, network expenses are saved, energy consumption is reduced, and the use is reliable.
Specifically, the image capturing device 300 is disposed on the power transmission line, and may be disposed on or near the power transmission line, for example, on a power transmission tower. The image acquisition device 300 is used for acquiring the state image of the power transmission line, and can photograph the power transmission line in real time or according to a preset time interval to obtain the state image of the power transmission line, the occurrence of the missed detection condition can be effectively avoided by acquiring the state image in real time, and the workload of the image acquisition device 300 can be reduced by acquiring the state image according to the preset time interval. The type of the image capturing device 300 is not exclusive, and may be, for example, an optical camera or an infrared camera 320, and the optical camera may capture a visible light image of the power transmission line as a state image, and is simple to implement and intuitive in imaging. The infrared camera 320 can collect the infrared image of the power transmission line as a state image, and the temperature of the power transmission line is generally inconsistent with the temperature of the foreign object due to the fact that electric energy is transmitted in the power transmission line, so that the infrared image collected by the power transmission line as the state image can also be used as a basis for subsequently detecting whether the foreign object exists. It is understood that in other embodiments, the image acquisition apparatus 300 may be of other types, such as an ultrasound imaging apparatus, etc., as long as the implementation is considered by those skilled in the art.
The processor 100 is also disposed on the power transmission line, and may be disposed on or near the power transmission line, for example, on a power transmission tower, and configured to perform target detection on the status image and send the detected status image with the foreign object to the cloud server 200. After acquiring the state image, the processor 100 performs image analysis on the state image to determine whether a foreign object exists on the power transmission line. The image analysis process is not unique and can include gray scale processing and contour analysis, and whether foreign matters exist on the power transmission line or not is judged by analyzing the gray scale of each target object of the state image and the contour of the object. When the processor 100 analyzes that a foreign object exists in the state image, it is considered that the foreign object invasion of the power transmission line is detected for the first time, and then a processing signal is sent to the cloud server 200 for subsequent processing. The processor 100 is also not unique in type, and may be, for example, a micro control unit, which can implement data processing, and has a small size and a high degree of integration. It is understood that in other embodiments, the micro control unit may be of other configurations or types as long as the implementation is deemed possible by one skilled in the art.
The cloud server 200 is also called a cloud server, and a higher-level algorithm can be stored in the cloud server, so that when an image is analyzed, the accuracy of an analysis detection result can be improved. In this embodiment, after receiving the processing signal, the cloud server 200 determines whether to issue an alarm according to the processing signal. According to different types of the processing signals, the process of performing alarm judgment by the cloud server 200 is different. For example, when the image capturing apparatus 300 includes an RGB camera, when the processor 100 analyzes the state image from the RGB camera, and detects that there is a state image of a foreign object, the state image of the foreign object is sent to the cloud server 200, and the processing signal at this time is the state image of the foreign object. The cloud server 200 performs a target review on the state image with the foreign object to determine whether the foreign object is actually present in the state image with the foreign object. And if the foreign matters exist, issuing an alarm. Specifically, the method for performing the target review is not unique, for example, the target review may be performed by using a common image processing process, including image gray processing, contour extraction and analysis, or the target review may be implemented by using an artificial neural network, for example, a convolutional neural network, which may be specifically selected according to actual requirements, as long as the skilled person in the art considers that the target review may be implemented. Generally, the algorithm used by the cloud server 200, such as an artificial neural network, is complex, the target rechecking precision is high, more types of objects can be identified, and the accuracy of the foreign object detection result can be further ensured.
Or, when the image acquisition device 300 includes the RGB camera 310 and the infrared camera 320, when the processor 100 analyzes the state image from the RGB camera and the state image from the infrared camera 320, and after the state images with the foreign object are detected, the processor 100 sends an alarm signal to the cloud server 200, and the processing signal at this time is an alarm signal, and the cloud server may not analyze the image after receiving the alarm signal, and directly issues an alarm, so as to save the control process. It can be understood that, when the image acquisition device 300 includes the RGB camera and the infrared camera 320, when the processor 100 analyzes the state image from the RGB camera and the state image from the infrared camera 320, and detects that there is a foreign object in only the state image from one camera, the state image in which there is a foreign object is sent to the cloud server 200 for target retest, the processing signal at this time is the state image in which there is a foreign object, the cloud server 200 further determines whether there is a foreign object intrusion condition in the power transmission line, and if it is determined that there is a foreign object intrusion condition, an alarm is issued. Therefore, the influence of the working error of the image acquisition device 300 on the detection result can be reduced, and the working reliability of the foreign matter detection equipment of the power transmission line is improved.
Further, the manner of issuing the alarm by the cloud server 200 is not unique, for example, sending a short message or making a call to the operation and maintenance personnel to inform the operation and maintenance personnel that the power transmission line has a foreign object intrusion, and the foreign object intrusion needs to be handled in time. Or, the cloud server 200 may also send an alarm signal to the connected alarm device, and the alarm device may be an audible and visual alarm or a display screen, and inform of the intrusion of the foreign object in time.
In one embodiment, referring to fig. 1, the processor 100 includes a microcontroller unit 110 and an embedded neural network processing unit 120, the image capturing device 300 and the embedded neural network processing unit 120 are both connected to the microcontroller unit 110, the microcontroller unit 110 is in communication with the cloud server 200, the microcontroller unit 110 is configured to receive a state image and transmit the state image to the embedded neural network processing unit 120, the embedded neural network processing unit 120 performs object detection on the state image, and after detecting that there is a state image with a foreign object, a processing signal is sent to the cloud server 200 through the microcontroller unit 110.
Specifically, in this embodiment, the microcontroller unit 110 may serve as a repeater, and is responsible for sending the received state image to the embedded neural network processing unit 120, and sending the image processed by the embedded neural network processing unit 120 to the cloud server 200. The embedded neural network processing unit 120 is mainly used as the image processor 100, different devices realize different operations, and the problem that the service life of the device is influenced by overlarge workload of a single device can be avoided. The embedded neural network processing unit 120 adopts a decision mode similar to the human brain, has a deep learning function, and is small in computing power and small in size. When the embedded neural network processing unit 120 performs target detection on the state image, a convolutional neural network algorithm may be used, and a suitable target detection algorithm needs to be selected according to the specificity of hardware of the embedded neural network processing unit 120. Further, the embedded neural network processing unit 120 may be embedded in a micro control unit, which increases the degree of integration of the processor 100 and reduces the size of the processor 100.
In one embodiment, image capture device 300 includes an RGB camera 310 and an infrared camera 320, both RGB camera 310 and infrared camera 320 being coupled to processor 100.
Specifically, the image capturing device 300 may include an RGB camera 310 and an infrared camera 320, where the RGB camera 310 captures three basic color components of red, green and blue by three different cables, and three separate CCD sensors are usually used to obtain three color signals, which may be used to capture an accurate color image. The infrared camera 320 can collect the infrared image of the power transmission line as a state image, and the temperature of the power transmission line is generally inconsistent with the temperature of the foreign object due to the fact that electric energy is transmitted in the power transmission line, so that the infrared image collected by the power transmission line as the state image can also be used as a basis for subsequently detecting whether the foreign object exists. Further, the number of the RGB cameras 310 may be two or more, the number of the infrared cameras 320 may also be two or more, and each of the RGB cameras 310 and the infrared cameras 320 may be disposed at different positions, so as to realize multi-position and multi-angle state image acquisition of the power transmission line, thereby facilitating improvement of accuracy of foreign object detection results. In addition, RGB camera 310 and infrared camera 320 have the same angle of shooting, and RGB camera 310 and infrared camera 320 and testing result can verify each other, reduce the detection error, improve transmission line foreign matter check out test set's working property.
In one embodiment, the processor 100 is configured to segment the status image from the RGB camera 310 and perform object detection on the segmented sub-images. The processor 100 may further send the state image of the detected sub-image with the foreign object to the cloud server 200, and after the cloud server 200 segments the received state image with the foreign object, the segmented sub-image is subjected to target rechecking, and if the foreign object is identified, an alarm is issued.
Specifically, in the online monitoring scene of the power transmission line, the scene is often relatively open, and the ratio of the objects to be monitored in the picture is relatively small, so that when the processor 100 performs the target detection on the state image from the RGB camera 310, the state image is firstly segmented to obtain more than two sub-images, and then the segmented sub-images are detected, so that the accuracy of the detection result can be improved. If the sub-image is detected to have the foreign object, the foreign object is considered to be detected in the state image of the sub-image, and the state image of the sub-image is used as a processing signal and is sent to the cloud server 200 for further processing. It can be understood that, when the processor 100 detects the target from the state image of the infrared camera 320, the state image from the infrared camera 320 is usually not high in resolution, so that the target detection can be directly performed without performing the step of image segmentation. Similarly, after receiving the processing signal, the cloud server 200 performs target retest on the state image with the foreign object, divides the state image with the foreign object to obtain two or more sub-images, then detects the divided sub-images, and if the foreign object is detected in the sub-images, it is determined that the foreign object is indeed present in the state image with the foreign object in the sub-images, and issues an alarm. By adopting the mode of firstly segmenting the image and then analyzing and processing the sub-image, the small target in the image can be more easily detected, and the missing rate can be reduced.
In one embodiment, processor 100 is configured to segment the status image from RGB camera 310 using a bisection or octagon method.
Specifically, when the processor 100 divides the state image from the RGB camera 310, the state image is generally divided into two or more sub-images with equal areas, so that the same or similar analysis method can be applied to each sub-image, which is beneficial to reducing the image processing error. Further, in this embodiment, the processor 100 may segment the state image by a bisection or octadentation method, where the segmented sub-image is close to 1: the proportion of 1 can effectively prevent the proportion of objects in the state image from changing, and is more favorable for target detection. The cloud server 200 may segment the state image with the foreign object by a bisection or octadentation method, where the sub-image after the segmentation is close to 1: the proportion of 1 can effectively prevent the proportion of objects in the state image with foreign matters from changing, and is more beneficial to target detection. Taking the image capturing device 300 as the RGB camera 310 as an example, if the resolution of the image captured by the RGB camera 310 is 2048 × 1080, the image may be divided into 1024 × 1080 halves (see fig. 2) or 512 × 540 eights (see fig. 3), and the target detection is performed on the divided image, and the target detection result is recorded.
In one embodiment, the processor 100 is configured to perform object detection on the status image based on a first weight, and the cloud server 200 is configured to perform object review on the received status image with the foreign object based on a second weight, where parameters of the first weight are less than parameters of the second weight.
Specifically, before the processor 100 performs target detection on the state image and before the cloud server 200 performs target retest on the received state image with the foreign object, the artificial neural network model weight may be pre-trained, and as shown in fig. 5, the training process includes: firstly, artificial neural network structure construction and weight initialization are carried out, then forward training is carried out, a loss function is calculated, when the precision reaches the expectation, the weight at the moment is taken as the weight, when the precision does not reach the expectation, backward propagation is carried out, the weight is updated, and then the step of forward training is returned. After the image acquisition device 300 acquires the state image, the intruding object labeling may be performed on the object appearing in the image, for example, to identify the forest fire and the kite, i label the forest fire and the kite appearing in the image, and if no related object appears in the image, the labeling is not performed, and the labeling process is generally manual. After the invader is labeled, image preprocessing is performed, wherein the image preprocessing is an important step in the artificial neural network training process, and the preprocessing method of the image can influence the training precision and is an important part of model training when operations such as scaling, combination and the like are performed on the image. After image pre-processing, go back to the forward training step as described above.
The process of training the weights of the artificial neural network models used by the processor 100 and the cloud server 200 is substantially the same, and the difference is that the artificial neural network used by the cloud server 200 is generally complex, the target detection precision is high, and more objects can be identified. The artificial neural network used in the processor 100 has a simpler network structure, and after the weights are obtained by training, quantization and format conversion of the weights are required to realize real-time monitoring with low energy consumption. The weight training is generally performed on a computer with better performance, and the trained weight is issued to the cloud server 200 and the processor 100 for use.
In this embodiment, the processor 100 is configured to perform target detection on the status image based on a first weight, and the cloud server 200 is configured to perform target review on the received status image with the foreign object based on a second weight, where a parameter of the first weight is less than a parameter of the second weight. The first weight and the second weight are different because the network depth used by the processor 100 is shallower than that used on the cloud server 200, the parameter of the first weight is less than that of the second weight, the processor 100 needs less memory for performing target detection on the state image based on the first weight, and the energy consumption for detecting the target once is lower.
When image capture device 300 includes RGB camera 310 and infrared camera 320, processor 100 may use different weights for the RGB image and the infrared image, for example, the weight used for the RGB image is weight a and the weight used for the infrared image is weight a. Correspondingly, the weight used by the cloud server for the RGB image is weight B, and the weight used for the infrared image is weight B.
Above-mentioned transmission line foreign matter check out test set, transmission line foreign matter check out test set includes image acquisition device 300, treater 100 and cloud server 200, image acquisition device 300 connects treater 100, treater 100 and cloud server 200 communication, image acquisition device 300 and treater 100 all set up in the transmission line, image acquisition device 300 is used for gathering transmission line's state image and sending to treater 100, treater 100 is used for carrying out the target detection to the state image, after detecting the state image that has the foreign matter, send processing signal to cloud server 200, cloud server 200 is used for judging whether to issue according to processing signal and reports an emergency and asks for help or increased vigilance. Whether foreign matters exist on the primary power transmission line is firstly identified through the image acquisition device 300 and the processor 100, if so, a processing signal is sent to the cloud server 200, the cloud server 200 judges whether to issue an alarm according to the processing signal, the number of images sent to the cloud server 200 can be reduced, dependence on a high-reliability network is reduced, network expenses are saved, energy consumption is reduced, and the use is reliable.
For a better understanding of the above embodiments, the following detailed description is given in conjunction with a specific embodiment. In one embodiment, the power transmission line foreign object detection device includes an image acquisition apparatus 300, a processor 100 and a cloud server 200, the processor 100 includes a microcontroller unit 110 and an embedded neural network processing unit 120, and the image acquisition apparatus 300 includes an RGB camera 310 and an infrared camera 320. Referring to fig. 4, the RGB camera 310 and the infrared camera 320 are both connected to the microcontroller unit 110, so as to implement multi-angle shooting of the current situation of the power transmission line, wherein the RGB camera 310 and the infrared camera 320 have the same shooting angle. The embedded neural network processing unit 120 processes the pictures to obtain a preliminary prediction result, and solves the problem of small target detection by means of image segmentation. If the early warning object appears in the picture shot by the RGB camera 310 and the prediction result of the picture shot by the infrared camera 320, directly issuing warning information to the cloud server 200; if early warning articles appear in one of the prediction results, the images acquired by the two are uploaded to the cloud server 200, and whether the power transmission line is invaded by foreign matters or not is finally judged and an alarm is issued. The neural network of the cloud server 200 itself is more advanced. The reason for identifying once at the edge device is to reduce the dependence on the high-reliability network and save the network charge.
The embedded neural network processing unit 120 and the cloud server 200 need to perform pre-training of artificial neural network model weights before performing a target detection task, and the overall process is as shown in fig. 5. The image labeling refers to labeling objects appearing in an image, for example, identifying a forest fire and a kite, labeling the forest fire and the kite appearing in the image, and if no related object appears in the image, not labeling. All labeling processes can be manual. The image preprocessing is a necessary step in the artificial neural network training process, and the images are required to be scaled, combined and the like. The preprocessing method of the image can affect the training precision and is an important part of model training. The loss function and the accuracy are expected to have no fixed value according to actual conditions. The weight training is performed on a computer with better performance, and the weights obtained by training are issued to the embedded processing unit and the cloud server 200. The output result is the coordinates and type of the foreign object recognized in the image, and if there is no foreign object in the image, no output is made.
The flows of the artificial neural network model weights used by the embedded neural network processing unit 120 and the cloud server 200 are substantially the same, and the different points are that the artificial neural network used by the cloud server 200 is complex, the target detection precision is high, and more types of objects can be identified. The artificial neural network used in the embedded neural network processing unit 120 has a simpler network structure, and after the weights are obtained by training, quantization and format conversion of the weights are required to realize real-time monitoring with low energy consumption. The weight used by the embedded neural network processing unit 120 is a weight a, i.e., a first weight, and the weight used by the cloud server 200 is a weight B, i.e., a second weight. The artificial neural network used in the embedded neural network processing unit 120 and the artificial neural network used by the cloud server 200 may be convolutional neural networks or others. Further, when the image capturing apparatus 300 includes the RGB camera 310 and the infrared camera 320, the weight used by the embedded neural network processing unit 120 for the RGB image is a weight a, and the weight used for the infrared image is a weight a ×; the weight used by the cloud server 200 for the RGB image is weight B, and the weight used for the infrared image is weight B.
In the scene of on-line monitoring of the power transmission line, the scene is often wide, the occupation of the objects to be monitored in the picture is small, and the mode of detecting the small target in the scene of the power transmission line after dividing the image is adopted. The resolution of an image which can be acquired by an RGB camera used by the power transmission line integrated sensor is 2048 × 1080, the image is divided into 512 × 540 eight equal parts or 1024 × 1080 two equal parts, target detection is respectively carried out on the divided image, target detection results are recorded, and an image division schematic diagram is shown in the specification. The scale of the pictures of the input model is specified, typically 1: 1, so in the image pre-processing described above, there is a part of the work to scale the image out of scale. Although the scaled image may also be input into the model, the scale of the object has changed, which is not good for object recognition. For example 2048 x 1080 pictures, scaled to 1: 1, 1080 x 1080, the whole picture becomes narrow, and objects in the picture can also become narrow accordingly, so that the recognition effect is influenced. It is appropriate to divide the graph into 2 and 8 equal parts, and the cut small graph is close to 1: 1, in a ratio of 1.
And performing target detection on the segmented sub-images, wherein the resolution of the sub-images is reduced after the segmentation mode, and the image proportion is close to 1: 1, the influence of zooming on the target is small during preprocessing of the image, and the small target in the image is easy to detect. By adopting two segmentation modes of eight equal parts and two equal parts, on one hand, the influence of image segmentation on the detection of a large target object can be avoided, and on the other hand, the small target object can be prevented from being just positioned in the middle of a segmentation line and being segmented. This may not be done because the resolution at which the infrared camera 320 takes pictures is typically low.
When monitoring foreign matter intrusion at the side end, if the pictures shot by the RGB camera 310 and the pictures shot by the infrared camera 320 both detect early warning objects, the early warning information is directly issued to the cloud server 200, if an early warning object appears in a prediction result of one of the pictures, the state picture of the power transmission line used for the detection is uploaded to the cloud server 200 for further detection, whether the power transmission line is invaded by the foreign matter is finally judged, and an alarm is issued, if the cloud server 200 also judges that the early warning object exists in the picture, the alarm is issued, and the specific flow is shown in fig. 4. The edge side is at the edge side, i.e. the power line side, as opposed to the cloud side, which is at the cloud server 200 side.
Transmission line foreign matter detection equipment passes through little the control unit, imbeds neural network processing unit 120, a plurality of RGB cameras 310, and infrared camera 320 and high in the clouds server 200 and realizes transmission line foreign matter invasion monitoring, and wherein RGB camera 310 and infrared camera 320 have the same shooting angle. The plurality of RGB cameras 310 are connected with the microcontroller unit 110 to realize multi-angle shooting of the current situation of the power transmission line, the embedded neural network processing unit 120 processes the pictures to obtain a preliminary prediction result, and the problem of small target detection is solved in an image segmentation mode. If the early warning object appears in the picture shot by the RGB camera 310 and the prediction result of the picture shot by the infrared camera 320, directly issuing warning information to the cloud server; if one prediction result shows an early warning object, the images acquired by the two prediction results are uploaded to a cloud server, and the cloud server finally judges whether the power transmission line is invaded by foreign matters and issues an alarm.
By embedding the neural network processing unit 120, the multiple RGB cameras 310 and the infrared camera 320, real-time and low-energy-consumption edge target detection is realized, the problem of small target detection in a large scene is solved by segmenting a high-resolution RGB image and carrying out target identification on segmented sub-images, and the problems of high requirement on network state and high network communication expense are solved by only uploading the images with early-warning objects to the cloud server 200. The cloud server 200 is used for carrying out target detection on the transmission line state picture which is transmitted by the side equipment and is possibly invaded by the foreign matter, so that misjudgment and missing judgment of the transmission line foreign matter caused by insufficient calculation force and simple network model used by the side equipment are solved, and the accuracy of the transmission line foreign matter invasion detection task is further improved. By means of simultaneous detection of the RGB camera 310 and the infrared camera 320, the accuracy of target detection is improved.
Based on the same inventive concept, the embodiment of the application further provides a foreign matter detection method of the power transmission line foreign matter detection device, the power transmission line foreign matter detection device comprises an image acquisition device 300, a processor 100 and a cloud server 200, the image acquisition device 300 is connected with the processor 100, the processor 100 is communicated with the cloud server 200, and the image acquisition device 300 and the processor 100 are both arranged on the power transmission line; the foreign object detection method of the power transmission line foreign object detection device is applied to the processor 100, please refer to fig. 6, and the method includes:
step S110: and acquiring a state image of the power transmission line.
Specifically, the processor 100 is connected to the image capturing device 300, and may capture a status image of the power transmission line through the image capturing device 300. The type of the image capturing device 300 is not exclusive, and may be, for example, an optical camera or an infrared camera 320, and the optical camera may capture a visible light image of the power transmission line as a state image, and is simple to implement and intuitive in imaging. The infrared camera 320 can collect the infrared image of the power transmission line as a state image, and the temperature of the power transmission line is generally inconsistent with the temperature of the foreign object due to the fact that electric energy is transmitted in the power transmission line, so that the infrared image collected by the power transmission line as the state image can also be used as a basis for subsequently detecting whether the foreign object exists.
Step S120: and carrying out target detection on the state image.
After acquiring the state image, the processor 100 performs image analysis on the state image to determine whether a foreign object exists on the power transmission line. The image analysis process is not unique and can include gray scale processing and contour analysis, and whether foreign matters exist on the power transmission line or not is judged by analyzing the gray scale of each target object of the state image and the contour of the object.
Step S130: and after the state image with the foreign matter is detected, sending a processing signal to the cloud server, so that the cloud server judges whether to issue an alarm or not according to the processing signal.
When the processor 100 analyzes that a foreign object exists in the state image, it is considered that the foreign object invasion of the power transmission line is detected for the first time, and then a processing signal is sent to the cloud server 200, so that the cloud server 200 judges whether to issue an alarm or not according to the processing signal. When the image capturing device 300 includes an RGB camera, the processor 100 analyzes the state image from the RGB camera, and after detecting that there is a state image of a foreign object, sends the state image of the foreign object to the cloud server 200, where the processing signal is the state image of the foreign object.
Or, when image acquisition device 300 includes RGB camera and infrared camera 320, when processor 100 analyzes the state image from RGB camera and the state image from infrared camera 320, and after all detecting the state image that there is the foreign object, processor 100 sends an alarm signal to cloud server 200, and the processing signal at this moment is an alarm signal, and the cloud server may not analyze the image after receiving the alarm signal, and directly alarms to save the control flow. It can be understood that, when the image acquisition device 300 includes the RGB camera and the infrared camera 320, when the processor 100 analyzes the state images from the RGB camera and the state images from the infrared camera 320, and detects that there is a foreign object in only one state image from one camera, the state images with the foreign object are sent to the cloud server 200 for target rechecking, and the cloud server 200 further determines whether there is a foreign object intrusion condition in the power transmission line. Therefore, the influence of the working error of the image acquisition device 300 on the detection result can be reduced, and the working reliability of the power transmission line foreign matter detection equipment is improved.
The alarm issuing mode is not unique, for example, the alarm issuing mode is to send a short message or make a call to the operation and maintenance personnel to inform the operation and maintenance personnel that the power transmission line is invaded by a foreign object and needs to be processed in time. Or, the cloud server 200 may also send an alarm signal to a connected alarm device, which may be an audible and visual alarm or a display screen, and so on, to inform the situation of the intrusion of the foreign object in time.
In one embodiment, referring to fig. 7, step S120 includes step S121.
Step S121: and after the state image is segmented, respectively carrying out target detection on the segmented sub-images.
When the processor 100 performs target detection on the state image, the state image is firstly segmented to obtain more than two sub-images, then the segmented sub-images are detected, if a foreign object exists in the sub-images, the processor considers that the foreign object is detected in the state image of the sub-images, and sends the state image of the sub-images to the cloud server 200 for further processing. By adopting the mode of firstly segmenting the image and then analyzing and processing the sub-image, the small target in the image can be more easily detected, and the missing rate can be reduced.
Based on the same inventive concept, the embodiment of the present application further provides a foreign object detection method for the power transmission line foreign object detection device, the power transmission line foreign object detection device includes an image acquisition device 300, a processor 100 and a cloud server 200, the image acquisition device 300 is connected to the processor 100, the processor 100 is in communication with the cloud server 200, both the image acquisition device 300 and the processor 100 are disposed on the power transmission line, the foreign object detection method for the power transmission line foreign object detection device is applied to the cloud server 200, please refer to fig. 8, and the method includes:
step S210: a processed signal is received.
The processing signal is processed by the processor 100 to perform target detection on the state image, and the state image is sent after the state image with the foreign matter is detected, and the state image is acquired by the image acquisition device 300 through the power transmission line. The type of the processing signal is not unique, and when the image capture device 300 includes an RGB camera, the processor 100 analyzes the state image from the RGB camera, and after detecting that there is a state image of a foreign object, sends the state image of the foreign object to the cloud server 200, where the processing signal is the state image of the foreign object. Or, when the image capturing device 300 includes an RGB camera and an infrared camera 320, after the processor 100 analyzes the state image from the RGB camera and the state image from the infrared camera 320 and detects that there is a state image of a foreign object, the processor 100 sends an alarm signal to the cloud server 200, where the processing signal is an alarm signal. When the image acquisition device 300 comprises the RGB camera and the infrared camera 320, when the processor 100 analyzes the state images from the RGB camera and the state images from the infrared camera 320, and detects that foreign matters exist in only one state image from one camera, the state images in which the foreign matters exist are sent to the cloud server 200 for target review, and the processing signal at this time is the state image in which the foreign matters exist.
Step S220: and judging whether to issue an alarm or not according to the processing signal.
The cloud server 200 is also called a cloud server, and a higher-level algorithm can be stored in the cloud server, so that when an image is analyzed, the accuracy of an analysis detection result can be improved. In this embodiment, after receiving the processing signal, the cloud server 200 determines whether to issue an alarm according to the processing signal. According to different types of the processing signals, the process of performing alarm judgment by the cloud server 200 is different. For example, when the image capturing apparatus 300 includes an RGB camera, when the processor 100 analyzes the state image from the RGB camera, and detects that there is a state image of a foreign object, the state image of the foreign object is sent to the cloud server 200, and the processing signal at this time is the state image of the foreign object. The cloud server 200 performs a target recheck on the state image in which the foreign object exists to determine whether the foreign object exists in the state image in which the foreign object exists. And if the foreign matters exist, issuing an alarm. Specifically, the method for performing the target review is not unique, for example, the target review may be performed by using a common image processing process, including image gray processing, contour extraction and analysis, or the target review may be implemented by using an artificial neural network, for example, a convolutional neural network, which may be specifically selected according to actual requirements, as long as the skilled person in the art considers that the target review may be implemented. Generally, the algorithm used by the cloud server 200, such as an artificial neural network, is complex, the target rechecking precision is high, more types of objects can be identified, and the accuracy of the foreign object detection result can be further ensured.
Or, when the image capturing device 300 includes the RGB camera and the infrared camera 320, when the processor 100 analyzes the state image from the RGB camera and the state image from the infrared camera 320, and after the state image with the foreign object is detected, the processor 100 sends an alarm signal to the cloud server 200, and the processing signal at this time is an alarm signal, and the cloud server may not analyze the image after receiving the alarm signal, and directly issues an alarm to save the control process. It can be understood that, when the image acquisition device 300 includes the RGB camera and the infrared camera 320, when the processor 100 analyzes the state image from the RGB camera and the state image from the infrared camera 320, and detects that there is a foreign object in only the state image from one camera, the state image in which there is a foreign object is sent to the cloud server 200 for target retest, the processing signal at this time is the state image in which there is a foreign object, the cloud server 200 further determines whether there is a foreign object intrusion condition in the power transmission line, and if it is determined that there is a foreign object intrusion condition, an alarm is issued. Therefore, the influence of the working error of the image acquisition device 300 on the detection result can be reduced, and the working reliability of the power transmission line foreign matter detection equipment is improved.
The alarm issuing mode is not unique, for example, the alarm issuing mode is to send a short message or make a call to the operation and maintenance personnel to inform the operation and maintenance personnel that the power transmission line is invaded by a foreign object and needs to be processed in time. Or, the cloud server 200 may also send an alarm signal to a connected alarm device, which may be an audible and visual alarm or a display screen, and so on, to inform the situation of the intrusion of the foreign object in time.
In one embodiment, the image capturing device includes an RGB camera 310 and an infrared camera 320, see fig. 9, and step S220 includes step S221.
Step S221: when the processing signal includes a first detection result obtained by the processor performing the target detection on the state image from the RGB camera and a second detection result obtained by the processor performing the target detection on the state image from the infrared camera 320, an alarm is issued.
It is understood that the first detection result here is that foreign matter is detected in the state image from the RGB camera 310, and the second detection result is that foreign matter is detected in the state image from the infrared camera 320. When image acquisition device 300 includes RGB camera and infrared camera 320, when treater 100 carries out the analysis to the state image that comes from the RGB camera and the state image that comes from infrared camera 320, all detect the state image that has the foreign matter after, treater 100 sends alarm signal to cloud server 200, and the processing signal at this moment is alarm signal, and the cloud server can not carry out the analysis to the image after receiving alarm signal, directly issues the warning to practice thrift control flow.
It is to be understood that, in other embodiments, when the image acquisition apparatus 300 includes an RGB camera and an infrared camera 320, if the processor 100 analyzes the status images from the RGB camera and the status images from the infrared camera 320, and detects that a foreign object exists in only one of the status images from the cameras, the status image with the foreign object exists is sent to the cloud server 200 for target review, the processing signal at this time is the status image with the foreign object, the cloud server 200 performs target review on the status image with the foreign object, further determines whether the power transmission line really has a foreign object intrusion condition, and if it is determined that the foreign object intrusion condition does exist, issues an alarm. Therefore, the influence of the working error of the image acquisition device 300 on the detection result can be reduced, and the working reliability of the power transmission line foreign matter detection equipment is improved.
When the cloud server 200 performs target retesting on the state image with the foreign matter, the state image with the foreign matter is firstly segmented to obtain more than two sub-images, then the segmented sub-images are detected, if the foreign matter is detected to exist in the sub-images, the sub-images are considered to be in the state image with the foreign matter, and an alarm is issued. By adopting the mode of firstly segmenting the image and then analyzing and processing the sub-image, the small target in the image can be more easily detected, and the missing rate can be reduced.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. The foreign matter detection equipment for the power transmission line is characterized by comprising an image acquisition device, a processor and a cloud server, wherein the image acquisition device is connected with the processor, the processor is communicated with the cloud server, and the image acquisition device and the processor are arranged on the power transmission line;
the image acquisition device is used for acquiring the state image of the power transmission line and sending the state image to the processor, the processor is used for carrying out target detection on the state image, and sending a processing signal to the cloud server after the state image with the foreign matter is detected, and the cloud server is used for judging whether to issue an alarm or not according to the processing signal.
2. The device for detecting the foreign matters on the power transmission line according to claim 1, wherein the processor comprises a microcontroller unit and an embedded neural network processing unit, the image acquisition device and the embedded neural network processing unit are both connected with the microcontroller unit, and the microcontroller unit is communicated with the cloud server;
the microcontroller unit is used for receiving the state image and transmitting the state image to the embedded neural network processing unit, the embedded neural network processing unit carries out target detection on the state image, and after the state image with foreign matters is detected, a processing signal is sent to the cloud server through the microcontroller unit.
3. The power transmission line foreign matter detection device according to claim 1, wherein the image acquisition device comprises an RGB camera and an infrared camera, and both the RGB camera and the infrared camera are connected to the processor.
4. The device for detecting foreign matters on an electric transmission line according to claim 3, wherein the processor is configured to segment the state image from the RGB camera and perform target detection on the segmented sub-images respectively.
5. The transmission line foreign object detection apparatus of claim 4, wherein the processor is configured to segment the status image from the RGB camera using a bisection or octadentation method.
6. The device for detecting the foreign object on the power transmission line according to claim 1, wherein the processor is configured to perform target detection on the state image based on a first weight, the cloud server is configured to perform target recheck on the received state image with the foreign object based on a second weight, and a parameter of the first weight is smaller than a parameter of the second weight.
7. The foreign matter detection method of the power transmission line foreign matter detection equipment is characterized in that the power transmission line foreign matter detection equipment comprises an image acquisition device, a processor and a cloud server, wherein the image acquisition device is connected with the processor, the processor is communicated with the cloud server, and the image acquisition device and the processor are both arranged on a power transmission line;
the foreign matter detection method of the power transmission line foreign matter detection equipment is applied to the processor, and the method comprises the following steps:
acquiring a state image of the power transmission line;
carrying out target detection on the state image;
and after the state image with the foreign matter is detected, sending a processing signal to the cloud server, so that the cloud server judges whether to issue an alarm or not according to the processing signal.
8. The method for detecting the foreign object in the power transmission line foreign object detection device according to claim 7, wherein the performing the target detection on the state image comprises:
and after the state image is segmented, respectively carrying out target detection on the segmented sub-images.
9. The foreign matter detection method of the power transmission line foreign matter detection equipment is characterized in that the power transmission line foreign matter detection equipment comprises an image acquisition device, a processor and a cloud server, wherein the image acquisition device is connected with the processor, the processor is communicated with the cloud server, and the image acquisition device and the processor are both arranged on a power transmission line;
the foreign matter detection method of the power transmission line foreign matter detection device is applied to the cloud server, and the method comprises the following steps:
receiving a processing signal; the processing signal is used for carrying out target detection on a state image by the processor, the state image with foreign matters is sent after being detected, and the state image is acquired by the power transmission line through the image acquisition device;
and judging whether to issue an alarm or not according to the processing signal.
10. The foreign object detection method of the power transmission line foreign object detection apparatus according to claim 9, wherein the image acquisition device includes an RGB camera and an infrared camera, and the determining whether to issue the warning according to the processing signal includes:
and issuing an alarm when the processing signal comprises a first detection result obtained after the processor performs target detection on the state image from the RGB camera and a second detection result obtained after the processor performs target detection on the state image from the infrared camera.
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