Abstract
The misinformation between the actual public lighting equipment and those reported by municipalities to the electricity companies may result in a kind of loss mainly referred as commercial loss. The usual adopted procedure to minimize this problem is sending technician teams to the field to do a low effective inspection of the lighting points. This scenario motivated the development of the methodology described in this chapter. The approach comprises both hardware and software elements, and were developed to give electricity companies more precise information about the actual luminaries and bulbs installed on the lighting poles, with a high degree of automation. The hardware is composed mainly by a set of light sensors, managed by a digital signal processor. The selection of the sensors and the overall hardware architecture are described, as well as the issues associated to the communication between the data acquisition module and an external CPU unit with data storage and user interface attributes. In sequence, the data mining techniques and pattern recognition strategies are discussed. In order to establish the elements for the required training and validation procedures, a laboratory environment was developed to reproduce some on-the-field configurations. The results of the laboratory experiments and field tests are analyzed and discussed. These results conducted to the development of the software components that integrate the system.
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Soares, G.M. et al. (2015). On the Use of Light Sensors and Pattern Recognition Techniques for Automated Detection of Street Lighting Lamps. In: Mason, A., Mukhopadhyay, S., Jayasundera, K. (eds) Sensing Technology: Current Status and Future Trends III. Smart Sensors, Measurement and Instrumentation, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-10948-0_4
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DOI: https://doi.org/10.1007/978-3-319-10948-0_4
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