Automatic parameter selection of image reconstruction algorithms for planar array capacitive imaging

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Planar array capacitance imaging sensor design optimisation

Electrical Capacitance Tomography (ECT) is used in many industries as a non-invasive detection and measurement method which works by .nding permittivity changes in a viewing region. This usually consists of sensor electrodes surrounding a region of interest however this is not always possible as sometimes the viewing region is only accessible from a single side. In this case a planar array ECT system can be used where the electrodes are all laid out on a co-planar surface. Initial simulation results indicated some features of sensor design which might aid image reconstructions. 5 new electrode con.gurations were designed which incorporated these features in different ways. The designs were tested on their ability to reproduce a wooden block suspended in air and a water bottle buried in sand. Their performance was judged based on distance/depth detection of the block/water bottle and the shape of the reconstruction. Singular Value Decomposition (SVD) analysis was also performed on each sensor design to show their theoretical ability to reproduce the permittivity of the viewing region. Previously similar work man-aged to reliably reconstruct objects at distances of 60mm from the sensor. But in this paper, with a smaller sensor head, up to 90mm was achieved with good accuracy. Combining the sensor designs together into a single sensor created the Combined Sensor which was able to reconstruct objects with a much greater reliability and was less susceptible to error or noise. With the right set up the Combined Sensor was able to achieve up to 120mm of depth detection. Also the combined sensor was able to detect a buried water bottle in sand up to 50mm. Further simulation results on the Combined Sensor indicated that using up to 100 unique sensor con.gurations in the combined sensor was bene.cial but after 100 the amount of additional information gained was not signi.cant. The results showed that the sensor head design can be optimised in order to produce improved reconstruction for planar array ECT. This improvement means that planar array ECT could potentially become a viable option for applications such as landmine detection, particularly .nding non-metallic objects which can not be picked up with conventional landmine detection techniques such as metal detectors.<br/

Automatic parameter selection of image reconstruction algorithms for planar array capacitive imaging

Landmines are often be made out of plastic with almost no metallic components which makes detection difficult. A plausible solution is to detect superficial buried plastic objects using planar array electrical capacitance tomography (ECT). Distance detection is a big limiting factor of planar array ECT. Given the ill-posedness and loss of sensitivity with depth, regularization, and optimal selection of reconstruction parameters are required for detection. In this paper, we propose an 'automatic parameter selection' (APS) method for image reconstruction algorithms that selects optimal parameters based on the input data based on a three step process. The aim of the first two steps is to provide an approximate estimate of the parameters so that future reconstructions can be performed quickly in step 3. To optimize the reconstruction parameters the APS method uses the following metrics. Front surface distance detection (FSDD) is a method of determining an accurate distance measurement from sensor head to object surface in low resolution image reconstructions using interpolation between voxels and Otsu thresholding. Cross-section reconstruction score (CSRS) is a simple binary image comparison method which calculates a ratio of expected image to reconstructed image. An initial set of capacitance data was taken for an object at various distances and used to train the APS method by finding the best reconstruction parameters for each distance. Then, another set of capacitance data was taken for a new object at different distances than before and reconstructed using the parameters selected by the APS method. The results of this showed that the APS method was able to select unique parameters for each reconstruction which produced accurate FSDDs and consistent CSRSs. This has taken away the need for an expert to manually select parameters for each reconstruction and sped up the process of reconstructions after training. The introduction of FSDD and CSRS is useful as they accurately describe how reconstructions were score and will allow future work to compare results effectively.</p