[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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Thermal Camera for Autonomous Mobile Platforms 109 , (4) , (5) where and are digital values of detectors response for higher ( ) and lower ( ) temperature of uniform reference black body, and are mean values of array response for temperatures TH and TL of black body, given by: 1 . (6) Fig. 17. Block diagram of a hardware NUC correction Fig. 18. Sample thermal image before (a) and after (b) NUC correction On the basis of analysis and simulations the 14-bit resolution of video data (resolution of ADC converter) and 16-bit correction coefficients were adopted [3, 4]. From equation (3) came off that the digital module realizing TPC algorithm
110 G. Bieszczad et al. has to make one multiplication and one addition operation. To increase the precision of NUC calculations (which are made on fixed-point digits) the coefficients must be resized accordingly. Block diagram of hardware realization of TPC algorithm for thermal camera is presented on Fig. 17. In this hardware realization the correction is realized “on the fly”. Time of calculation is shorter than the time between reading succeeding detectors. The module is designed to have inputs and outputs compatible with internal VideoBus link. Non-uniformity correction module was described in VHDL language as a parameterized entity. D signer can change (when needed) the parameters of the module like size of actual infrared detector array, length of video data word and length of coefficient data word. Because of that feature this module can be used for any array size. Fig. 18 shows two thermal images: one taken before and one after NUC correction performed by the described module. 5 Calibration, Measurement and Determination of Thermal Camera Parameters Procedure used to determine the correction coefficients according to TPC algorithm is performed on a test stand [9] with two blackbodies at temperatures TL and TH, respe tively. The stand with mounted camera is kept at constant ambient temperature (e.g. in climate chamber). After switching on the camera, prior to measurements it is ne essary to wait until the array and camera housing temperatures stabilize. Then the calibration procedure may begin. The test stand used for the determination of NUC coefficients is shown in Fig. 19. Averaged array response values for TL and TH temperatures, used for NUC coefficient calculations, are called calibration points. Basic calibration points are the array averaged responses for the temperatures TL = 20°C and TH = 40°C. Calculated correction coefficients Gij i Oij are stored in camera’s internal memory. In each detector array a faulty pixels (detectors) can be found, due to manufacturing defects. Those detectors, referred to as “bad pixels” are usually found during calibration. There are several ways to identify bad pixels: • on the basis of detector gain, • on the basis of detector offset, • on the basis of detector noise. Identification of bad pixels on the basis of detector gain is performed using a threshold value. A pixel is considered bad if its gain coefficient in a NUC table differs from nominal value by more than selected percentage value. For example, if the threshold value is 25% then all the detectors with gain coefficients below 0.75 and above 1.25 are considered bad.
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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6 A. Babiarz et al. The description
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12 A. Babiarz et al. a) b) Fig. 7.
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14 A. Babiarz et al. • The camera
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18 A. Babiarz et al. a) b) Fig. 11.
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Recognition and Location of Objects
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186 D. Bereska et al. 4.1.3 Open-Lo
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188 D. Bereska et al. Fig. 16. Comp
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Probabilistic Approach to Planning
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206 Practical Applications of Class
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Prototyping the Autonomous Flight A
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Feature Extraction and HMM-Based Cl
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252 K. Daniec et al. by the ability
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Technology Development of Military
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344 Conclusions Control algorithms
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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