WO2018211396A1 - Détection de lignes électriques dans des images aériennes - Google Patents

Détection de lignes électriques dans des images aériennes Download PDF

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Publication number
WO2018211396A1
WO2018211396A1 PCT/IB2018/053330 IB2018053330W WO2018211396A1 WO 2018211396 A1 WO2018211396 A1 WO 2018211396A1 IB 2018053330 W IB2018053330 W IB 2018053330W WO 2018211396 A1 WO2018211396 A1 WO 2018211396A1
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Prior art keywords
powerline
image
aerial image
aerial
module
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PCT/IB2018/053330
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English (en)
Inventor
S. N. Omkar
J. Senthilnath
K. N. Ramesh
Anurag Jain
Abhishek Kumar
Gautham ANAND
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Indian Institute Of Science
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Publication of WO2018211396A1 publication Critical patent/WO2018211396A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/176—Urban or other man-made structures
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
    • G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
    • G08B25/08—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
    • G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
    • G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems

Definitions

  • the present disclosure relates generally to the field of powerline surveillance. More specifically, the present disclosure relates to a system and method for detection of powerlines in aerial images.
  • LIDAR sensors are however costly and bulky thereby increasing the size of the UAV and the subsequent increased cost of data acquisition. LIDARs also suffer from issues in quality of data acquisition. Areas such as dense canopies cannot be detected by LIDAR.
  • UAV unmanned aerial vehicle
  • a manned vehicle for instance, a helicopter.
  • the present disclosure relates generally tothe field of powerline surveillance. More specifically, the present disclosure relates to a system and method fordetection of powerlines in aerial images, more particularly, UAV based remote sensed images.
  • An aspect of the present disclosure relates to a system for detection of a powerline in an aerial image, the system including a non-transitory storage device having embodied therein one or more routines operable to detect the powerline in the aerial image, andone or more processors coupled to the non-transitory storage device, wherein the one or more routines include an image retrieval module, which when executed by the one or more processors, obtains at least one aerial image including the powerline, a binary classified image generation module, which when executed by the one or more processors, subjects said at least one aerial image to a pixel-by-pixel analysis, a skeletonization module, which when executed by the one or more processors, enables skeletonization of said binary classified image to reduce one or more objects of said at least one aerial image to a median line, a geometric feature determination module, which when executed by the one or more processors, calculates any or a combination of a shape index and a density index for the powerline in the skeletonized image based on one or
  • system further includes a region growing module that subjects the skeletonized image to region growing segmentation to restore continuity of the one or more objects lost during the skeletonization.
  • the binary classified image generation module is trained using a plurality of RGB spectral values to differentiate between pixels corresponding to powerline features and pixels corresponding to non-powerline features.
  • the powerline detection module is configured to classify the one or more objects in the aerial image as non-powerline feature in case the calculated shape index pertaining to the powerline in the aerial image is below a pre-determined limit.
  • the powerline detection module is configured to classify the one or more objects in the aerial image as non-powerline feature in case the calculated density index pertaining to the powerline in the aerial image is above a pre-determined limit.
  • the skeletonization module is further configured to decrease density of the powerline in the binary classified image.
  • Another aspect of the present disclosure relates to a method for detection of a powerline in an aerial image, the method including the steps of (i) obtaining, by a computing device, at least one aerial image including the powerline, (ii) obtaining, by the computing device, a binary classified image by subjecting said at least one aerial image to a pixel -by- pixel analysis, (iii) effecting, by the computing device, skeletonization of said binary classified image to reduce one or more objects of said at least one aerial image to a median line, (iv) calculating, by the computing device, any or a combination of a shape index and a density index for the powerline in the skeletonized image, and (v) detecting, by the computing device, the powerline in the aerial image by removing any non-powerline feature from the aerial image based on comparison of any or a combination of the calculated shape index and the calculated density index with a pre-determined set of shape index and density index of the powerline.
  • the aerial image is an aerial vehicle based remote sensed image acquired by an image capturing device operatively coupled with the aerial vehicle.
  • the method further includes a step of subjecting the skeletonized image to region growing segmentation to restore continuity of the one or more objects lost during the skeletonization step.
  • the step of removing any non-powerline feature to effect detection of the powerline in the aerial image further includes the steps ofclassifying the one or more objects in the aerial image as non-powerline feature in case the calculated shape index pertaining to the powerline in the aerial image is below a pre-determined limit, andclassifying the one or more objects in the aerial image as non-powerline feature in case the calculated density index pertaining to the powerline in the aerial image is above a predetermined limit.
  • FIG. 1A through FIG. IF illustrate aerial images including powerlines in accordance with an embodiment of the present disclosure.
  • FIG. 2 illustrates an exemplary representation depicting functional modules of the system for detection of a powerline in an aerial imagein accordance withembodiments of the present disclosure.
  • FIG. 3 illustrates an exemplary UAV and payload in accordance with an embodiment of the present disclosure.
  • FIG.4 illustrates an exemplary block diagram depicting controller of the UAV system in accordance with an embodiment of the present disclosure.
  • FIG. 5 illustrates an exemplary flowchart depicting a method of detection of a powerline in an aerial imagein accordance with an embodiment of the present disclosure.
  • FIG. 6A illustrates an exemplary aerial image including powerlines
  • FIG. 6B illustrates an exemplary output image from the binary classified image generation module
  • FIG. 6C illustrates an exemplary output image from the skeletonization module
  • FIG. 6D illustrates an exemplary output image from theregion growing module
  • FIG. 6E illustrates an exemplary output image from the geometric image classifying module
  • FIG. 6F illustrates an exemplaryoverlapped Extreme Learning Machines (ELM) segmented image with original input image.
  • ELM Extreme Learning Machines
  • FIG. 7 A through FIG. 7F illustrate output for six different images in accordance with embodiments of the present disclosure.
  • FIG. 8 illustrates a graphical representation of performance parameters (measures) with different number of hidden neurons in accordance with an embodiment of the present disclosure.
  • FIG. 9 illustrates the statistical visualization of performance of the method in comparison with ground reference data in accordance with an embodiment of the present disclosure.
  • the present disclosure relates to a system and method of detection of powerlines in aerial images.
  • An aspect of the present disclosure relates to a system for detection of a powerline in an aerial image, the system including a non-transitory storage device having embodied therein one or more routines operable to detect the powerline in the aerial image, andone or more processors coupled to the non-transitory storage device, wherein the one or more routines include an image retrieval module, which when executed by the one or more processors, obtains at least one aerial image including the powerline, a binary classified image generation module, which when executed by the one or more processors, subjects said at least one aerial image to a pixel-by-pixel analysis, a skeletonization module, which when executed by the one or more processors, enables skeletonization of said binary classified image to reduce one or more objects of said at least one aerial image to a median line, a geometric feature determination module, which when executed by the one or more processors, calculates any or a combination of a shape index and
  • the aerial image is an aerial vehicle based remote sensed image acquired by an image capturing device operatively coupled with the aerial vehicle.
  • system further includes a region growing module that subjects the skeletonized image to region growing segmentation to restore continuity of the one or more objects lost during the skeletonization.
  • the binary classified image generation module is trained using a plurality of RGB spectral values to differentiate between pixels corresponding to powerline features and pixels corresponding to non-powerline features.
  • the powerline detection module is configured to classify the one or more objects in the aerial image as non-powerline feature in case the calculated shape index pertaining to the powerline in the aerial image is below a pre-determined limit.
  • the powerline detection module is configured to classify the one or more objects in the aerial image as non-powerline feature in case the calculated density index pertaining to the powerline in the aerial image is above a pre-determined limit.
  • the skeletonization module is further configured to decrease density of the powerline in the binary classified image.
  • Another aspect of the present disclosure relates to a method for detection of a powerline in an aerial image, the method including the steps ofobtaining, by a computing device, at least one aerial image including the powerline, obtaining, by the computing device, a binary classified image by subjecting said at least one aerial image to a pixel -by-pixel analysis, effecting, by the computing device, skeletonization of said binary classified image to reduce one or more objects of said at least one aerial image to a median line, calculating, by the computing device, any or a combination of a shape index and a density index for the powerline in the skeletonized image, anddetecting, by the computing device, the powerline in the aerial image by removing any non-powerline feature from the aerial image based on comparison of any or a combination of the calculated shape index and the calculated density index with a pre-determined set of shape index and density index of the powerline.
  • the aerial image is an aerial vehicle based remote sensed image acquired by an image capturing device operatively coupled with the aerial vehicle.
  • the method further includes a step of subjecting the skeletonized image to region growing segmentation to restore continuity of the one or more objects lost during the skeletonization step.
  • the step of removing any non-powerline feature to effect detection of the powerline in the aerial image further includes the steps ofclassifying the one or more objects in the aerial image as non-powerline feature in case the calculated shape index pertaining to the powerline in the aerial image is below a pre-determined limit, andclassifying the one or more objects in the aerial image as non-powerline feature in case the calculated density index pertaining to the powerline in the aerial image is above a predetermined limit.
  • FIG. 1 A through FIG. IF illustrate aerial images including powerlines acquired at varying altitudes and angles in accordance with an embodiment of the present disclosure to better represent the challenges faced while detecting the powerlines in the aerial images.
  • the RGB values of far-away point of powerline may get mismatched with other non powerline features, which shows spectral-classification challenge.
  • FIG. IB illustrates an exemplary image captured when the image capturing device is at different angle and highlighted area showing a light pole with almost same RGB values as of powerline and hence, face the same above mentioned problem.
  • FIG. 1C illustrates an exemplary image captured at a relatively higher altitude, wherein it can be seen that the middle portion of powerline is not clearly visible and highlighted area shows existence of the same problem as presented hereinabove. Highlighted portion of FIG. ID illustrates a street light having almost same RGB values as of powerline and hence, may create problem in classification.
  • FIG. IE illustrates the overlapped powerlines
  • FIG. IF illustrates the overlapping of mud patches with powerline pixels.
  • the proposed system and method realized in accordance with embodiments of the present disclosure can help overcome such inaccuracies amongst others and can find utility in surveillance of powerline(s). Accordingly, the present disclosure can envisage autonomous surveillance of powerlines to detect any physical fault therein.
  • FIG. 2 illustrates an exemplary representation depicting functional modules of the system for detection of a powerline in an aerial image in accordance withembodiments of the present disclosure.
  • the system includes a non-transitory storage device having embodied therein one or more routines operable to detect the powerline in the aerial image, andone or more processors coupled to the non-transitory storage device.
  • the one or more routines include an image retrieval module 202, which when executed by the one or more processors, obtains at least one aerial image including the powerline, a binary classified image generation module 204, which when executed by the one or more processors, subjects said at least one aerial image to a pixel-by-pixel analysis, a skeletonization module 206, which when executed by the one or more processors, enables skeletonization of said binary classified image to reduce objects of said at least one aerial image to a median line and to decrease density of the powerline, a region growing module 208, which when executed by the one or more processors, applies region growing to restore continuity of the objects lost during the skeletonization, a geometric feature determination module 210, which when executed by the one or more processors, calculates any or a combination of a shape index and a density index for the powerline, and a powerline detection module212, which when executed by the one or more processors, detects the powerline in the aerial image by removing any non-powerline feature from the aerial image
  • an aerial vehicle/UAV unmanned aerial vehicle
  • an image/video capturing device is used to capture video and/or still images including the powerline(s) as illustrated in FIG. 3.
  • the UAV 302 can be configured with an image/video capturing device 304 (shown separately from the UAV for better clarity) among other payload (not shown) to capture aerial images and/or video including the powerline(s) 306.
  • the UAV 302 is further configured with other instruments that can help in the surveillance of the powerline.
  • the UAV is configured with a thermal camera that can help detect leakage of current in the powerlines.
  • any other instruments as known to or appreciated by a person skilled in the art, can be configured with the UAV or aerial image/video capturing device without departing from the scope and spirit of the present disclosure.
  • the UAV used for remote sensing for powerline detection can be flown at low altitude and can be used for remote sensing.
  • Remote sensing from a UAV platform is commonly known as low altitude remote sensing (LARS).
  • LATS low altitude remote sensing
  • fixed-wing or rotary wing can be used.
  • small UAV is utilized along with small payload, which is more feasible and cost effective solution for powerline detection.
  • quad-copter UAV is used, which is a stable platformand suitable for aerial imaging. Their ability to hover and vertical takeoff can make them suitable for powerline monitoring applications.
  • the UAV may be a quad-copter built using off the shelf components (viz. remote receive 310, flight stabilizer 312, battery 314, brushless motor speed controller 316, brushless motor 318 and the likes) and modified to carry the payload and camera requirements.
  • the UAV belonging to class of multi-rotor aircrafts and powered by batteries is utilized to serve its intended purpose as laid down in disclosure of the present disclosure.
  • any other UAV can be utilized without departing from the scope and spirit of the present disclosure.
  • UAV with the flight endurance of around 20 minutes is utilized.
  • the flight maneuver of UAV is controlled remotely by a pilot using a remote control308.
  • the range of remote control is around 500 meters.
  • the weight of the quad-copter UAV is about 1.4kg including the camera payload.
  • the UAV that can be utilized to serve its intended purpose as laid down in embodiments of the present disclosure is with the specifications as shown in Table 1.
  • the camera 304 that can be utilized to serve its intended purpose as laid down in embodiments of the present disclosure is with the specifications as shown in Table 2.
  • the UAV includes 4 brushless DC motors affixed with propellers mounted on a quad-copter frame.
  • speeds of the motors can be governed by on-board electronic speed controllers (ESC) which can be powered by a special Lithium-Polymer battery.
  • ESC on-board electronic speed controllers
  • FIG. 4 illustrates an exemplary block diagram depicting controller of the UAV in accordance with an embodiment of the present disclosure.
  • Speed of the motors can help control the angular and linear velocity of UAV.
  • communication between UAV and remote control is of 2.4 GHz wireless receiver-transmitter pair.
  • pilot sends the control command through the control joysticks of the remote control 308, which encodes the signals and transmits it via radio waves.
  • the transmitted signals can be received by the on-board receiver 310 and decoded. These commands can then be interpreted by the on-board flight stabilized 12.
  • the on-board flight stabilizer 312 is an electrician based multiwii multi-copter firmware.
  • the flight stabilizer includes ATmega AVR 2560 microcontroller and MEMS based motion sensors for measuring the angular velocity and linear velocity of the quad-copter.
  • the MEMS based sensors are any or a combination of 3-axis gyroscope, 3-axis accelerometer, 3- axis magnetometer and 1-axis barometer.
  • the MEMS based sensors are 3- axis gyroscope, 3-axis accelerometer, 3-axis magnetometer and 1-axis barometer.
  • the flight stabilizer can obtain the angular velocity and linear velocity of UAV via the MEMS sensors and compares it with the desired state based on the commands sent by the pilot.
  • the flight stabilizer can then decide the speed of motors required to achieve the desired state of the UAV.
  • this comparison step is updated about every 3ms to achieve stable flight of UAV.
  • the comparison step can be performed continuously or at any interval to serve its purpose without departing from the scope and spirit of the present disclosure.
  • data acquisition is done with GoPro HD Hero2 (Go Pro, California, United States) camera304.
  • the camera measures about 98x58x34mm and weighs about 98grams.
  • the camera has the specifications asshown in Table 2.
  • focal length of the camera is fixed.
  • camera has a SD card slot onto which the recorded video can be stored.
  • Field of View of camera can be varied depending on the usage.
  • for acquiring the video camera was set to the video resolution of 1080p at 30fps.
  • Field of view was set to 127° in order to capture the powerlines.
  • UAV can be flown with the payload (camera) at a height of 25-30 meters above ground over the powerline to capture the video and/or still image(s).
  • camera 304 is fixed at the front end of the UAV 302 at an angle of 15 degrees in order to obtain the front view. Front view can allow the camera to capture the larger length of the powerline.
  • spatial resolution of the video varies with the altitude of the UAV.
  • the variation of altitude can be up to 5 meters, which can allow acquisition of images at varying altitudes.
  • the acquired video and/or still image(s) is analyzed to select the test images with good aerial view of the powerlines at different altitudes, angles and background.
  • the captured video or image(s) extracted therefrom and/or still image(s) viz. aerial images including powerline(s) are transmitted to the image retrieval module202.
  • the image retrieval module 202 receive/acquires/obtains the aerial images wirelessly from the image capturing device.
  • the captured video or image(s) extracted therefrom and/or still image(s) viz. aerial images including powerline(s) are provided as an input to the system 200 using methods as known to a person skilled in the art, for example, by operatively coupling the SD card of the UAV with the system 200.
  • the image retrieval module 202 is configured to communicate with the image/video capturing device associated with the UAV so as to receive the images therefrom.
  • the image retrieval module 202 is operatively coupled with the binary classified image generation module 204 to communicate aerial image(s) including powerline(s) thereto.
  • the binary classified image generation module 204 is subjected to Machine Learning.
  • the binary classified image generation module 204 may be an Extreme Learning Machine (ELM) thatis trained using dataset containing RGB values of the powerline and non-powerline classes.
  • ELM Extreme Learning Machine
  • the input samples have 3 -dimensional features; hence, there are 3 input nodes in the input layer and the number of output nodes in output layer is 2 as this is a binary classification.
  • An optimal number of hidden nodes can be chosen for hidden layer. Let the number of hidden nodes be H. Based on the number of hidden nodes, input weight matrix and bias matrix can be generated. Input weight matrix can be generated randomly based on the number of input nodes and hidden nodes. Dimensions of input weight matrix is Hx3. Bias matrix can also be generated randomly having dimensions Hxl.
  • Inputs to each hidden nodes can be calculated for the entire set of training data. The inputs can then be passed through the activation function to get the output of each hidden neuron. Output of hidden neuron can be given by
  • G(.) is the activation function for the hidden nodes
  • I h is the input applied to the logistic function.
  • Output weight matrix can be computed analytically from equation 6 below where H "1 is the pseudo-inverse of hidden layer output matrix and T is the target matrix.
  • the input and output weight matrices generated during training can be used to classify the image into powerline and non-powerline class.
  • Spectral value of each pixel (Pi) of test image can be applied as the input to the trained model.
  • Pi ⁇ Vi.r . Vi,g- Vi,b ⁇ ) wherein, P r , P l g , and / ⁇ denotes the red, blue and green pixel value of each sample.
  • Input matrix I H to the hidden nodes is computed using the equation where w is the input weight matrix and b is the bias of the hidden node
  • activation function applied to matrix I H results in matrix H 0 . It should be noted that activation function is same as the function (equation 4) used for training the model.
  • the hidden layer output matrix (H 0 ) is multiplied with output weight matrix (Wo) to determine the output labels corresponding to powerline and non-powerline class.
  • Yi is the resultant class type (powerline or non-powerline label) of the i th pixel.
  • the skeletonization module 206 is configured to effect skeletonization of said binary classified image to reduce objects of said at least one aerial image to a median line and to decrease density of the powerline.
  • Skeletonizationcan preserve the topological features and the original size of the objects.
  • Skeletonizationcan reduce the objects into a median line thereby reducing the density of the objects.
  • Skeletonization S(A) of an object can be given by
  • A is the connected object in the binary image and B is the structuring element.
  • S k is the skeleton point.
  • Object A is eroded k times until it is reduced to single pixel and each erosion is followed by opening the eroded image. The difference between the eroded image and opened image gives the skeleton points.
  • Union of all skeleton points gives the skeleton of the object A. Skeletonizationcan affect the powerline features causing discontinuity in the powerlines (including other objects).
  • loss of continuity is restored by the region growing module 108.
  • the region growing module 208 is configured to apply region growing to restore continuity of the objects lost during the skeletonization.
  • iterative region growing is applied after Skeletonization.
  • region growing is applied 1 time.
  • region growing is applied for a maximum of 3 times.
  • Powerlines are the continuous linear structures, whereas non-powerline features are the not continuous. Accordingly, geometrical featurescan be taken into consideration and Shape Index and/or Density Index can be determined/calculated/applied to bring out clear distinction between the two features (powerline and non-powerline).
  • the geometric feature determination module 210 is configured to calculate any or a combination of a shape index and a density index for the powerline (or any other object). In an embodiment, the geometric feature determination module 210 is configured to calculate both of a shape index and a density index for the powerline (or any other object). Shape Index (SI) and Density Index (DI) of an object can be given by
  • A is the area of the object
  • P is the perimeter of the object
  • Var(X) and Var(Y) represents variance in X and Y coordinates, which approximately represent the radius of the object.
  • the powerline detection module212 is configured to detect powerline in the aerial image and to remove any non-powerline feature from the aerial image. In an embodiment, the powerline detection module212 is configured to classify the objects in the aerial image based on the pre-determined shape index and/or density index. In an embodiment, high threshold of SI is set to remove the non-powerline feature due to the fact thatpowerline being thin and long, has larger perimeter and lesser area and hence, ratio of perimeter to area is very high. Accordingly, the powerline detection module212 is configured to classify the objects in the aerial image as non-powerline feature in case the SI is below a pre-determined limit. In an embodiment, low threshold of DI is set to remove the non- powerline feature as the same has less radius. Accordingly, the powerline detection module212 is configured to classify the objects in the aerial image as non-powerline feature in case theDI is above a pre-determined limit.
  • FIG. 5 illustrates an exemplary flowchart depicting a method of detection of a powerline in an aerial image.
  • at least one aerial image including the powerline is obtained by computing device followed by subjecting said at least one aerial image to a pixel-by-pixel analysis to obtain a binary classified image therefrom as shown at step 504.
  • Skeletonization of said binary classified image is done by a skeletonization module to reduce objects of said at least one aerial image to a median line and to decrease density of the powerline as shown at step 506.
  • Region growing is then applied by a region growing module to restore continuity of the objects lost during the skeletonization step as shown at step 508 followed by calculating any or a combination of a shape index and a density index for the powerline by a geometric feature determination module as shown at step 510.
  • Any non-powerline feature is removed by a powerline detection module to effect detection of the powerline in the aerial image as shown at the step 512.
  • FIG. 6A through FIG. 6F illustrates transformation of the aerial image to detect powerlines therefrom in accordance with an embodiment of the present disclosure.
  • An aerial image for example, UAV remote sensed input image
  • the trained binary classified image generation module output of which, i.e., binary classified image is illustrated in FIG. 6B.
  • FIG. 6B It can be observed that the binary classified image predominantly contains the powerline features. However, some misclassification can be observed as well.
  • the binary classified image as shown in FIG. 6B,is subjected to morphological operation (i.e., skeletonization), output from which is as shown in FIG. 6C.
  • morphological operation i.e., skeletonization
  • the skeletonized image is subjected to region growing operation, output from which is as shown in FIG. 6D.
  • the threshold value for shape index (SI) and density index(DI) may befixed at greater than 0.8 and less than 0.65, respectively.
  • FIG. 6E there is a marked improvement in classification after the application of shape indexand density index.
  • FIG. 6F illustrates an exemplary overlapped ELM segmented image with original input image.
  • Table 3 below exemplifies the threshold values for shape and density indices utilized for segmentation of image for final extraction of powerlines for different input aerial images as illustrated in FIG. 1 A through FIG. IF. Accordingly, a person skilled in the art would appreciate that any other threshold values for shape and density indices can be fixed/utilized to serve its intended purpose as laid down in embodiments of the present disclosure.
  • Table-3 Threshold values of shape and density indices for different images for ELM-SEG model
  • FIG. 7 A through FIG. 7F illustrates output for six different images (extracted and/or analyzed images overlapped on the real images) in accordance with embodiments of the present disclosure.
  • the performance parameters viz. completeness, correctness and quality are analyzed.
  • FIG. 8 illustrates various performance measures for trained binary classified image generation module (ELM model) with different number of hidden neurons after performing spectral-spatial measures.
  • ELM model trained binary classified image generation module
  • results indicate that with increase in number of hidden neurons in trained binary classified image generation module, the performance measures (parameters) gradually increase.
  • FIG. 8 illustrates the performance measure with different number of hidden neurons, as these are increasing gradually but at a certain level they are consistent.
  • FIG. 9 illustrates the statistical visualization of performance of ELM-SEG in comparison with ground reference data in accordance with an embodiment of the present disclosure.
  • the R2 value is found to be 0.817, which indicates a good fit.
  • the present disclosure provides a system and method to detect presence of powerlines in aerial images.
  • the present disclosure provides a system and method that effectively point out powerlines in an image captured by a camera device coupled with an aerial vehicle, such as an unmanned aerial vehicle (UAV) or a manned vehicle, for instance, a helicopter.
  • an aerial vehicle such as an unmanned aerial vehicle (UAV) or a manned vehicle, for instance, a helicopter.
  • the present disclosure provides a system and method that envisages to eliminate manual monitoring and inspection of powerlines by utilizing advanced machine learning and image processing methods of powerline detection in an image.
  • the present disclosure provides a system and method that overcomes deficiencies associated with conventional powerline monitoring and detection techniques.

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Abstract

L'invention concerne un système et un procédé de détection de lignes électriques dans des images aériennes. Le système de détection d'une ligne électrique dans une image aérienne comprend : un module de récupération d'image (202), pour obtenir au moins une image aérienne comprenant la ligne électrique ; un module de production d'image classifiée binaire (204), pour soumettre ladite image aérienne à une analyse pixel par pixel ; un module de squelettisation (206), conçu pour effectuer la squelettisation de ladite image classifiée binaire afin de réduire les objets de ladite image aérienne à une ligne médiane ; un module d'agrandissement de zone (208), conçu pour appliquer l'agrandissement de zone, afin de restaurer la continuité des objets perdus lors de la squelettisation ; un module de détermination de caractéristique géométrique (210), pour calculer un indice de forme et un indice de densité pour la ligne électrique dans l'image squelettisée ; et un module de détection de ligne électrique (212), conçu pour éliminer toute caractéristique de non ligne électrique de ladite image aérienne.
PCT/IB2018/053330 2017-05-13 2018-05-14 Détection de lignes électriques dans des images aériennes WO2018211396A1 (fr)

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CN109544544A (zh) * 2018-11-30 2019-03-29 长讯通信服务有限公司 一种基于深度学习与无人机的移动通信维护对象状态识别方法
CN110509293A (zh) * 2019-09-06 2019-11-29 北京云迹科技有限公司 工作环境分析方法、装置及机器人
CN115219852A (zh) * 2022-09-19 2022-10-21 国网江西省电力有限公司电力科学研究院 一种无人机配电线路的故障智能研判方法

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WO2014147041A1 (fr) * 2013-03-18 2014-09-25 Delta Drone Procede de surveillance de l'etat de vegetation aux abords d'une infrastructure
WO2016187112A1 (fr) * 2015-05-15 2016-11-24 Airfusion, Inc. Appareil portable et procédé d'assistance à la décision pour fusion et analyse de données de capteurs multiples automatisées en temps réel
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WO2014147041A1 (fr) * 2013-03-18 2014-09-25 Delta Drone Procede de surveillance de l'etat de vegetation aux abords d'une infrastructure
WO2016187112A1 (fr) * 2015-05-15 2016-11-24 Airfusion, Inc. Appareil portable et procédé d'assistance à la décision pour fusion et analyse de données de capteurs multiples automatisées en temps réel
US20170092109A1 (en) * 2015-09-30 2017-03-30 Alarm.Com Incorporated Drone-augmented emergency response services

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109544544A (zh) * 2018-11-30 2019-03-29 长讯通信服务有限公司 一种基于深度学习与无人机的移动通信维护对象状态识别方法
CN110509293A (zh) * 2019-09-06 2019-11-29 北京云迹科技有限公司 工作环境分析方法、装置及机器人
CN115219852A (zh) * 2022-09-19 2022-10-21 国网江西省电力有限公司电力科学研究院 一种无人机配电线路的故障智能研判方法
CN115219852B (zh) * 2022-09-19 2023-03-24 国网江西省电力有限公司电力科学研究院 一种无人机配电线路的故障智能研判方法

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