19th International Conference on Mechatronics and Machine Vision in Practice (M2VIP12), 28-30th Nov 2012, Auckland, New-Zealand

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1 Monitoring and Remote Sensing of the Street Lighting System Using Computer Vision and Image Processing Techniques for the Purpose of Mechanized Blackouts Pooya Najafi Zanjani 1,2, Vahid Ghods 2, Morteza Bahadori 1 1. Tehran Electrical Distribution Company, Iran 2. Islamic Azad University Science And Research Branch In Semnan, Iran pooya_najafi_tech@yahoo.com, v.ghods@semnaniau.ac.ir Abstract Street lighting system as one of the foremost facilities used in the cities plays a pivotal role in reducing nocturnal car accidents and enhancing individual and social security. Therefore, one of the main priorities in Electrical Distribution Companies is the timely repair and maintenance of the street lightening which should be regularly checked. The common mechanized monitoring of the street lighting is based on measuring the consumed current of each lamp and comparing it with the lamp nominal current. In this way, the error in network is identified. Monitoring street lighting in Iran is presently conducted by traditional inspection and check-out method. This paper suggests a new method for monitoring and remote sensing of the street lighting system which is completely isolated from electricity network and applies picture shooting of the street lighting network, image processing, and computer vision techniques and separating the light source method in the picture. The system performance was simulated and verified by MATLAB software. The results indicate the ability and effectiveness of this method and led to closing a research contract with Tehran Electrical Distribution Company for pilot implementation of the plan in one of the passageways in Tehran. and other external parameters, we can control the brightness intensity of the lamps to decrease energy consumption. In addition, since the monitoring and tele-management systems constantly reports the system defaults, we will benefit from time-saving, less maintenance costs and higher system reliability[2]. Monitoring of street lighting network is based on measuring the amperage of each lamp and comparing it with the pre-determined reference values. The data collected through power line communication are sent to the local controller and enables identification of the default in lighting network[3]. The figure 1 shows the components of a smart street lighting network. Keywords-Street Lighting System, Remote Sensing, Image Processing, Smart Electrical Grids I. INTRODUCTION Street lighting is advantageous to the society in a variety of ways such as reducing nighttime car accidents and injuries, decreasing street crimes and vandalism, increasing the fear to commit a crime, popularizing public transportation, providing the possibility for nocturnal economic, educational and entertainment activities, facilitating rescue missions and enhancing closed-circuit television (CCTV) effectiveness [1]. Owing to the mentioned reasons, the timely repair and maintenance of the street lighting network is one of the major duties of the electrical distribution companies. Applying network connection abilities and automation to street lighting network and relying on weather condition data, traffic volume Figure 1. Components of a street lighting network monitoring and controlling system The system described in figure 1, is completely operational and benefits from satisfactory flexibility. It can bring about considerable energy-saving by monitoring the defaults in street lighting network and controlling the lamp brightness intensity. However, since the major part of the current street lighting network is old and time-worn which belongs to 20 to 30 years ago, implementing the mentioned plan entails renewing most of the facilities and incurring high costs ISBN:

2 Moreover, due to the complexity of the single line map of the low voltage lines and street lighting and feeding the lamps by different feeders and the necessity to install electronic ballasts with the ability to decrease brightness (dimming) [4] and network,controlling and measurement devices on each lamp post as well as the necessity to install current measurement instruments (Shunt Resistor, CT, Hall Effect Sensor) directly on the power distribution lines and the possibility of measurement instruments damages due to 2-phase power line, installing these facilities in some cases and is problematic and uneconomical. for monitoring of street lighting network and eradicating the defects has been already named, Also, decreasing installation and maintenance costs and providing a satisfactory economic justification, the plan of monitoring and remote sensing of street lighting network of Tehran Electrical Distribution Company using computer vision and image processing techniques for mechanized blackouts have been discussed. This method as no executive record. The schematic plan and main idea of the method is depicted in figure 2. To monitor the street lighting network for remote sensing and monitoring street lighting system, a network camera will be installed on an appropriate height from the related street lighting network area. well as the related results will be discussed in detail. Moreover, the mentioned plan has been registered in Iran`s Organization of Inventions and Industrial Properties on July 8th, 2012 with the registration number II. THE PROPOSED METHOD For picture shooting of street lighting network a network camera is used. This camera can be described as a combination of a camera and a computer in an integrated unit. The internal components of the camera include optical devices, image sensor, compression unit, operating system with the ability to connect to network, server, Web and FTP and other required facilities. Also, this camera does not need to be installed on a computer and can be connected to the network through IP independently and send the data to the target computer. Therefore, this camera is able to send pictures wherever there is a network based on IP whether wired, wireless or fiber optics [5]. The internal components of a network camera are depicted in figure 3. Figure 3. Internal components of a network camera Figure 2. The main idea of monitoring and remote sensing of street lighting network using computer vision and image processing techniques In this case, with selecting the location and using a lens with an appropriate focal length, the camera is able to imaging several kilometers of the depth and range of the street lighting network area. The data obtained from imaging is given to computer and the matrix for the picture is formed. Intelligent software uses computer vision and image processing techniques as well as the geographical databases related to the street lighting posts, filters the other light sources present in the picture and recognized the location of the street lamp posts belonging to Electrical Distribution Company in the picture and examines them being on or off. Afterwards, a mechanized report about the blackouts of the street lighting network is issued. In the following parts of the article, the executive methods, image processing and computer vision techniques as The visual data are digitally sent to the central processing unit through the telecom platform Point To Point Radio Link in an IP based network to transfer the picture of street lighting network, conduct the essential processes and recognize the blackouts [6]. Block diagram of the executive method and the facilities used for transferring the picture by the abovementioned method are depicted in the figures 4 and 5. Figure 4. Picture transfer from camera to the central computer Point To Point Radio Link telecom platform After picture shooting of the street lighting network, the data enter the central computer through telecom platform and will be analyzed by intelligent software using image processing techniques. 6

3 Figure 5. Transferring picture from several cameras to the central computer using Point To Point Radio Link telecommunication bed The necessary reports are issued in a mechanized manner after information extraction. The figure 6 shows the general block diagram of the system. Figure 8. Sample picture received from the street lighting network of Tehran streets lighting network and enables imaging a wider area of street lighting network including several highways and streets by installing the camera higher than the light fixture. The figure 9 shows the CUT-OFF effect in luminaire. Figure 6. The general block diagram of the system After the picture was received and entered into the memory of the central computer in form of RGB matrix, the phases below were designed and simulated in MATLAB software to detect the blackouts of the street lamps in the picture [7]. Block diagram of the software components is depicted in figure 7. Figure 9. Cut-off effect in luminaire First, the RGB picture is converted to a gray scale image depicted in fig. 10. Figure 7. Block diagram of blackout recognition software in street lighting In the following section, to verify the suggested method, a picture has been taken from an 8-meter altitude of the street lighting network of Tehran streets (figure 8) and image processing phase has been examined for recognizing the blackouts step-by-step. Regarding the light emission curve of the street lights, if the camera is installed lower than the light fixture, it can film a wide area of the street lighting network of a highway. Also, with regard to the NON CUT-OFF luminaire caused by reflector design of the light fixture, some parts of the light produced by the lamp are emitted to the upper space of the luminaire. This phenomenon contributes to the monitoring and remote sensing techniques of street Figure 10. Converting RGB picture to grayscale to use image processing techniques 7

4 At the next step, threshold filtering is applied and gray scale image is converted to binary image and the light sources present in the picture are displayed conspicuously. The result of this operation is shown in figure 11. assigned to the other light sources. The result is indicated in figure 13. Figure 11. Filtering the light sources with high brightness in the picture With regard to the proximity of the camera to the street lighting network and the high brightness of the street lamps in the picture, Low Pass Filter (LPF) has been to decrease the effects of small light sources such as cars lamps and adjacent building lamps. Using this filter led to the considerable decrease of noise and the small light sources in the picture. The result is indicated in figure 12. However, if the picture shooting camera is located far from the street lighting network, using low-pass filter leads to missing some of valuable data in the picture because the street lamps appear with low brightness in the picture. Therefore, it is not recommended to using low-pass filter in that case. Figure 13. Negative of image 12 At the next step, image labeling technique is implemented and the connected components in the picture are revealed and labeled. Also, their special location X, Y in the picture as well as their pixel numbers in each spot is examined. The components with fewer pixels are determined as small and unfavorable light sources and deleted. Afterward, the data extracted from the brightness spots, their special location and pixel number are saved in matrix form and analyzed. Table 1 depicts the extracted data from figure 8. Table1. Data extracted from image 8 related to Street lighting network Number X0 Y0 X1 Y1 Pixel Figure 12. Decrease in the effects of the noise and unfavorable small light sources in the picture Afterwards, to carry out the next step and use MATLAB predetermined functions, the image is converted to negative. With this operation in image matrix, a binary 1 is assigned to the bright spots related to street lighting and a binary 0 is The results of the table 1 show that 11 light sources have been identified in the picture after applying the filtering above. The result is indicated in figure 14. Without pixel filtering, software will detect 19 spotlights in picture. This result is indicated in figure 15. X0, Y0 in table 1 indicates specifications of where the bright spots begin and X1 and Y1 shows the specifications of where they end. To test the effectiveness, sensitivity and accuracy of the suggested algorithms, confusion matrix method has been used. In the scope of artificial intelligence, confusion matrix is used to examine the performance of the categorizing and identifying systems [8]. To display the results more clearly, the special data related to the starting and ending points of the bright 8

5 spots in figure 14 and figure 15 are drawn and numbered in red. Afterwards, to complete the confusion matrix, the table 2 was obtained using the results of the software outputs at this step without using software database. Figure 14. Displaying and numbering the revealed light spots with pixel filtering The results of the analysis based on figure 8 indicate that street lamps recognition algorithm without using database or computer learning techniques is able to do correct and incorrect categorization with 54% accuracy. It also can recognize the brightness spots of the street lamps with 54% accuracy. According to the results, it seems essential to use a special database which contains the location of light sources belonging to the street lamps in the picture in order to increase separability of the light sources in the image and enhance the performance and accuracy of the system. Thus, according to image 8, the database which contains special location X, Y of the brightness spots belonging to street lamps in the picture and the number of the constituent pixels is determined as the decision making reference according to the table 3 and the mechanized report is given to intelligent software and it is predicted that by combination of the image processing software result and an appropriate database light sources in the picture can be isolated with high accuracy and street lighting network can be monitored for the purpose of mechanized blackouts. Table 3. The database containing special information of the street lighting lamps in the picture Number X0 Y0 X1 Y1 Pixel III. CONCLUSION Figure 15. Displaying and numbering the revealed light spots without pixel filtering Results at this step (table 2) are the result of the software output without using software database. Table 2. Confusion matrix formation for accurate analysis of the algorithm without using database - + False FN=8 FP=5 True TN=0 TP=6 Accuracy Precision FN TP 73.7% (1) FN FP TN TP TP 54.5% (2) TP FP With regard to the necessity of timely maintenance of street lighting network and examination of the defaults of the current systems for street lighting network monitoring, implementing the plan of monitoring and remote sensing of street lighting network of Tehran Electrical Distribution Company using computer vision and image processing techniques for mechanized blackouts in some passageways seems to be crucial and economical. This plan can be completely isolated from the street lighting power network and conduct monitoring and remote sensing of a large number of passageway lamps which are fed by different branches using camera and transfer and data processing systems. In the nest phases of the project, other techniques will be examined for extracting data with high accuracy and increasing the reliability of remote sensing technique. REFERENCES [1] N. Smith, "Investing in street lighting; Investing in community infrastructure," Institution of Lighting Engineers (Midlands Region), UK, [2] H. Walraven and E. As, "Market Assessment and Review of Energy Savings," e-street light, Norway, [3] Norconsult, "Working tool for market players Guide for energy efficient intelligent outdoor lighting solutions," e-street light, Norway, [4] Jennic, "Intelligent street lighting," Jennic Ltd, Sheffield UK,

6 [5] A. C. AB, "Setting up an IP-Surveillance system using Axis network cameras, video encoders and AXIS Camera Station software," Axis Communications AB, Sweden, Lund, [6] A. T. Co, " Support & Download: Application Note: Wireless Surverillance Diagram," ALCON Telecommunications Co, [Online].Available: pdf. [Accessed ]. [7] R. C. Gonzalez, R. E. Woods and S. L. Eddins, Digital Image Processing Using MATLAB, USA: Gatesmark Publishing, [8] Kohavi and Provost, "Confusion Matrix," Department of Computer Science, Faculty of Science, University of Regina,1998.[Online].Available: _matrix.html. [Accessed ] 10